RNAdos calculates density-of-states summaries for RNA sequences, reporting representative structures and state counts across energy bands.
RNAfold predicts RNA secondary structure using minimum free energy (MFE) algorithms and optionally returns partition-function ensemble metrics when explicitly enabled.
RNALfold reports locally stable RNA secondary structures within a sliding window and returns their start and end positions on the input sequence.
RNAplfold computes local base pair probabilities using a sliding window approach. Useful for analyzing accessibility and identifying binding sites in long RNA sequences.
RNAsubopt enumerates all RNA secondary structures within a specified energy range above the minimum free energy (MFE). Useful for exploring the structural ensemble and identifying alternative conformations.
ViennaRNA supports a curated set of scientifically faithful ViennaRNA 2.7.2 workflows for RNA folding, density-of-states analysis, interaction prediction, local accessibility, plotting, inverse folding, and structure analysis.
RNAcofold predicts the joint secondary structure of two interacting RNA molecules and optionally reports partition-function and concentration-dependent equilibrium metrics.
RNAdistance compares RNA secondary structures using the selected native ViennaRNA distance representation and comparison mode.
RNAduplex computes the hybridization structure between two RNA sequences. Predicts the optimal duplex formation and binding energy.
RNAplex predicts fast query-target RNA interactions, reporting parsed hit coordinates, structures, and energies.
RNAdos calculates density-of-states summaries for RNA sequences, reporting representative structures and state counts across energy bands.
RNAfold predicts RNA secondary structure using minimum free energy (MFE) algorithms and optionally returns partition-function ensemble metrics when explicitly enabled.
RNALfold reports locally stable RNA secondary structures within a sliding window and returns their start and end positions on the input sequence.
RNAplfold computes local base pair probabilities using a sliding window approach. Useful for analyzing accessibility and identifying binding sites in long RNA sequences.
RNAsubopt enumerates all RNA secondary structures within a specified energy range above the minimum free energy (MFE). Useful for exploring the structural ensemble and identifying alternative conformations.
ViennaRNA supports a curated set of scientifically faithful ViennaRNA 2.7.2 workflows for RNA folding, density-of-states analysis, interaction prediction, local accessibility, plotting, inverse folding, and structure analysis.
RNAcofold predicts the joint secondary structure of two interacting RNA molecules and optionally reports partition-function and concentration-dependent equilibrium metrics.
RNAdistance compares RNA secondary structures using the selected native ViennaRNA distance representation and comparison mode.
RNAduplex computes the hybridization structure between two RNA sequences. Predicts the optimal duplex formation and binding energy.
RNAplex predicts fast query-target RNA interactions, reporting parsed hit coordinates, structures, and energies.
Configure inputs to begin
Set options on the left, then click “Submit job”.
RNAeval calculates the free energy of an RNA secondary structure given a sequence. Unlike structure prediction tools that find the optimal fold, RNAeval evaluates whether a proposed or known structure is thermodynamically favorable for a specific sequence.
This makes RNAeval essential for validating experimental structures, comparing alternative conformations, and verifying that designed sequences actually fold into their intended structures. The tool uses the same nearest-neighbor thermodynamic model as RNAfold, ensuring consistent energy calculations across the ViennaRNA suite.
RNAeval decomposes an RNA structure into its constituent loops and sums their energy contributions using experimentally measured nearest-neighbor parameters. Each structural element (stacked base pairs, hairpin loops, internal loops, bulges, and multi-branch loops) contributes to the total free energy based on its sequence context.
The nearest-neighbor model treats RNA stability as the sum of contributions from adjacent base pair stacks rather than individual pairs. A GC pair followed by an AU pair has different stability than two isolated pairs because stacking interactions between adjacent bases contribute significantly to the overall energy.
The default parameters come from the Turner 2004 dataset, derived from optical melting experiments on synthetic RNA oligomers. These parameters capture:
Free energy varies with temperature according to thermodynamic principles. At the default 37 degrees C (physiological temperature), the Turner parameters directly apply. At other temperatures, RNAeval rescales enthalpy and entropy contributions to compute adjusted free energies.
Higher temperatures destabilize structures (less negative free energy) because the entropic penalty of ordered structures becomes more significant. Lower temperatures favor more stable, ordered conformations.
Provide an RNA sequence and a secondary structure in dot-bracket notation, and RNAeval returns the free energy of that specific fold. This is useful for comparing alternative conformations, validating designed sequences, or checking whether an experimentally determined structure is thermodynamically favorable.
| Input | Description |
|---|---|
RNA Sequences | RNA sequence in FASTA format or plain text. Supports A, U, G, C nucleotides. |
Secondary Structure | Bracket notation representing the structure. Matching parentheses indicate base pairs; dots indicate unpaired positions. |
The structure must match the sequence length exactly. Each opening parenthesis pairs with the corresponding closing parenthesis, counting from left to right. For example, (((...))) describes a hairpin with a 3-bp stem and 3-nt loop.
| Setting | Description |
|---|---|
Temperature | Folding temperature in degrees Celsius (0-100, default 37). Affects free energy calculations through thermodynamic rescaling. |
RNAeval returns the calculated free energy in kcal/mol for each sequence-structure pair.
| Output | Description |
|---|---|
Sequence ID | Identifier from FASTA header or auto-generated |
Length | Number of nucleotides |
Structure | The evaluated secondary structure |
Energy | Free energy in kcal/mol. More negative values indicate more stable structures. |
| Energy (kcal/mol) | Interpretation |
|---|---|
| < -20 | Very stable structure, typical for longer RNAs with extensive base pairing |
| -10 to -20 | Moderately stable, common for functional RNA elements |
| -5 to -10 | Weakly stable, may sample alternative conformations |
| > -5 | Marginally stable, structure may not persist at physiological conditions |
These thresholds are approximate and depend heavily on sequence length. A 100-nt RNA with -15 kcal/mol is less stable per nucleotide than a 30-nt RNA with the same energy.
After predicting structures with RNAfold or obtaining them experimentally, RNAeval confirms whether the sequence energetically supports that fold. Large energy differences between predicted and evaluated structures suggest the sequence may not stably adopt the proposed conformation.
RNA molecules often exist in equilibrium between multiple structures. By evaluating different conformations for the same sequence, researchers can estimate population distributions. The Boltzmann distribution relates energy differences to relative populations:
A 1.4 kcal/mol difference at 37 degrees C corresponds to roughly a 10-fold population difference.
After using RNAinverse to design sequences for target structures, RNAeval verifies the designed sequence actually has favorable energy for the intended fold. Combined with RNAfold to check the minimum free energy structure, this confirms successful design.
RNAeval assumes the provided structure is valid and evaluable. It does not check for impossible base pairs (like two positions too close to pair) or validate that the structure represents a physically realizable fold.
The nearest-neighbor model has known limitations for non-canonical interactions, modified nucleotides, and certain structural motifs. Predictions for structures with extensive non-Watson-Crick pairs may be less accurate.
Energy calculations do not account for cellular context, ionic conditions beyond standard assumptions, or interactions with proteins or other molecules that may stabilize or destabilize specific conformations.