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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

RNAz

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.

MC-Fold-DP

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.

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.

Dynalign

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.

Rtools

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.

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.

ViennaRNA

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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.

RNAwolf

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.

Swellix

Allows RNA structure analysis. Swellix is a computational method, built on the Crumple algorithm, that combines helix abstraction with a combinatorial approach to RNA structure determination for computing all possible non-pseudoknotted structures for an RNA sequence. The software can incorporate constraints such as the minimum number and length of helices, from crystallography or cryoelectron microscopy experiments. It provides an alternative approach to RNA structure analysis when the assumptions of free energy minimization do not apply or when multiple conformations are present.

Advanced_multiloops

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.

CompaRNA

Provides an automated system for the continuous evaluation of RNA structure prediction methods. The goal of CompaRNA is to provide a ‘blind’ benchmark using experimental data before it becomes incorporated in the training data set of the assessed prediction methods. In analogy to protein structure prediction benchmarks Livebench and EVA, CompaRNA uses as a reference experimentally solved RNA structures deposited in the Protein Data Bank (PDB). It also provides the community with a ‘static’ benchmark, generated on a data set extracted from the RNAstrand database, which contains experimentally verified RNA secondary structures of any type and organism. RNAstrand includes a larger number of molecules than those with solved 3D structures, and it covers sequences that are on the average longer than those with known 3D structures. These benchmarks offer insight into the relative performance of different RNA secondary structure prediction methods on different types of RNA sequences and structures.

Pfold

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.

RNAlocopt

Samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. RNAlocopt is an efficient algorithm to compute the partition function over all secondary structures that are locally optimal in the Turner energy model. It constitutes a technical breakthrough in study of the folding landscape for RNA secondary structures. The main application of RNAlocopt is to sample structures from the low energy ensemble of locally optimal secondary structures, stochastically sampled by from the partition function of locally optimal structures, in a manner analogous to how Sfold samples from the ensemble of all secondary structures.

PR2S2Clust / Patched RNA-seq Read Segments' Structure-oriented Clustering

Extracts features to prepare the secondary structure profiles of the RNA-seq read segments. PR2S2Clust provides a strategy to employ the profiles to annotate the segments into ncRNA classes using several clustering strategies. The tool will help researchers to adopt the best strategy while they do intend to cluster their own segment data-sets and seek to focus on the structural perspective. Extensive real-world experiments over three publicly available RNA-seq datasets and a comparative analysis over four competitive systems confirm the effectiveness and superiority of the proposed system.

PredcircRNA

A machine learning approach focused on distinguishing circularRNA from other lncRNAs using multiple kernel learning. Firstly different sources of discriminative features were extracted, including graph features, conservation information and sequence compositions, ALU and tandem repeats, SNP densities and open reading frames (ORFs) from transcripts. Secondly, to better integrate features from different sources, a computational approach based on a multiple kernel learning framework was proposed to fuse those heterogeneous features.