Table 2.

List of some available programs for RNA secondary-structure prediction using a single-sequence or multiple-sequence comparison.

Program	Approach	Description	References
Prediction using a single sequence
Mfold	Free energy minimization	Determine the set of base pairs that gives the minimum free energy using dynamic programming methods	[87]
RNAfold	Free energy minimization	Predict the minimum free energy structure using the thermodynamic parameter approach; it can also estimate base pair probabilities	[86]
RNAstructure	Free energy minimization	Adapt experimental constraints such as chemical modification and SHAPE to improve prediction	[103]
MC-Fold	Free energy minimization	Assemble secondary structures from nucleotide cyclic motifs	[75]
Contrafold	Knowledge based	Predict structures using statistical data and training algorithms	[89]
Sfold	Knowledge based	Calculate a set of representative candidates using statistical sampling methods and Boltzmann assemble	[104]
RNAShapes	Knowledge based	Search the folding space using the reduced concept of RNA shape	[105, 106]
Kinwalker	Free energy minimization, kinetic folding	Build secondary structures using mimics of co-transcriptional folding and near optimal structures of subsequences	[107]
MPGAfold	Genetic algorithm	Search for possible folding pathways and functional intermediates using a massively parallel genetic algorithm	[108, 109]
Kinefold	Free energy minimization, stochastic folding simulations	Predict structures using a co-transcriptional folding simulation approach where RNA helices are closed and opened in a stochastic process; it can also predict pseudoknots	[112,113]
Pknots	Free energy minimization, parameter approximations	Predict pseudoknots using a dynamic programming algorithm and thermodynamic parameters augmented with approximated parameters for thermodynamic stability of pseudoknots	[111]
Prediction using multiple sequences
RNAalifold	Free energy minimization, covariation analysis	Determine a consensus secondary structure from a multiple-sequence alignment using covariation analysis	[94]
ILM	Free energy minimization, covariation analysis	Predict base pairs using an iterative loop matching algorithm, where base pairs are ranked using thermodynamic parameters and covariation analysis; it can also predict pseudoknots	[95]
CentroidFold	Knowledge based	Predict secondary structures using the centroid estimator employed by Sfold and the maximum expected accuracy estimator used by Contrafold	[90, 114]
Dynalign	Free energy minimization, pairwise alignment	Compute the lowest free energy sequence alignment and secondary structure common to two sequences	[76, 115,116]
Carnac	Free energy minimization, pairwise alignment	Predict the common structure shared by two homologous sequence using similarities, energy and covariations	[96]
RNAforester	Structural comparison using tree alignment models	Represent RNA secondary structures by tree graphs and compare and align secondary structures via alignment algorithms for trees and other graph objects	[93]
KNetfold	Machine learning	Determine pairs of aligned columns from a consensus using multiple-sequence alignment	[88]