CoFold is a thermodynamics-based RNA secondary structure folding algorithm that takes overall effects of co-transcriptional folding in account. It has been shown to significantly improve the state-of-art in terms of prediction accuracy, especially for long sequences greater than 1000 nt in length.
R-chie allows you to make arc diagrams of RNA secondary structures, allowing for easy comparison and overlap of two structures, rank and display basepairs in colour and to also visualize corresponding multiple sequence alignments and co-variation information. R4RNA is the R package powering R-chie, available forand local use for more customized figures and scripting.
RNA-Decoder is the first (and still only!) comparative method that explicitly takes the known protein-coding context of an RNA-sequence alignment into account when predicting evolutionarily conserved secondary-structure elements. These structure features may span both coding and non-coding regions. On known secondary structures, RNA-Decoder shows a sensitivity similar to the programs Mfold, Pfold and RNAalifold. So far, RNA-Decoder has been used to analyse the genomes of HCV, the polio virus, HIV and influenza A for functional RNA structure features, RNA-Decoder's results indicate a markedly higher specificity than Mfold, Pfold and RNAalifold.
SimulFold is a computer program for co-estimating an RNA structure including pseudo-knots, a multiple-sequence alignment and an evolutionary tree, given a set of evolutionarily related RNA sequences as input. In other words, you give SimulFold an initial alignment of RNA sequences as input and it will predict a consensus RNA structure (which may include pseudo-knots) while simultaneously estimating the sequence alignment and the evolutionary tree relating the RNA sequences. This means, in particular, that the input alignment need not be a manually curated alignment of high quality. SimulFold employs a Markov Chain Monte Carlo in order to sample from the joint posterior distribution of RNA structures, alignments and trees. A post-processing step is then used to cluster the sampled RNA structures into one RNA structure. The method is computationally extremely efficient compared tthe Sankoff-algorithm (which only caters for two input sequences and does not handle pseudo-knotted RNA structures).
Transat detects conserved helices of high statistical significance, including pseudo-knotted, transient and alternative structures. Given a multiple sequence alignment and a corresponding phylogenetic tree as input, Transat will recover all possible helices, assign a corresponding log-likelihood value and estimate a corresponding p-value which allows the predicted helices to be easily ranked.