RNA–RNA interaction prediction

What is RNA–RNA interaction prediction?

RNA–RNA interaction prediction is the computational identification of intermolecular base pairing between two RNA molecules, including likely binding sites, hybridization structures, interaction energies, accessibility costs, or a joint secondary structure. Different algorithms answer different questions, so duplex-only, accessibility-aware, and joint-folding results should remain distinct.

Use RNA–RNA interaction prediction to prioritize candidate pairs, locate likely contact regions, evaluate antisense or guide designs, or study miRNA, sRNA, lncRNA, and transcript interactions. The two sequence roles, orientation, transcript boundaries, and screening objective must be explicit.

An energetically favorable duplex is not proof that two RNAs meet or bind in a cell. Expression, localization, RNA-binding proteins, competing structures, modifications, kinetics, and concentration all affect biological interaction, so computational hits require orthogonal evidence.

When to use RNA–RNA interaction prediction

• Best fit. Candidate RNA pairs, target-site localization, antisense design, miRNA or sRNA regulation, and interaction screens
• Required context. Two RNA sequences with correct roles and boundaries plus temperature, interaction model, and validation plan

Benefits of RNA–RNA interaction prediction

• Locates candidate binding sites. Locates candidate binding sites while keeping the method and its native evidence explicit.
• Separates multiple interaction models. Separates multiple interaction models while keeping the method and its native evidence explicit.
• Supports pairwise target prioritization. Supports pairwise target prioritization while keeping the method and its native evidence explicit.

Primary limitations

• Cellular colocalization is absent. Cellular colocalization is absent; address it with appropriate context, controls, and orthogonal validation.
• Transcript boundaries affect results. Transcript boundaries affect results; address it with appropriate context, controls, and orthogonal validation.
• Energy scores are not binding proof. Energy scores are not binding proof; address it with appropriate context, controls, and orthogonal validation.

RNA–RNA interaction prediction methods

RNAduplex emphasizes an optimal intermolecular helix and hybridization energy. RNAup adds the energetic cost of opening intramolecular structure. RNAcofold predicts a joint two-strand secondary structure and can calculate ensemble or concentration-dependent terms when enabled.

RNAplex is designed for rapid query–target searches and reports hit coordinates and energies. Because each tool includes different structural terms and approximations, compare patterns and sites rather than sorting all raw scores in one shared ranking.

RNA–RNA interaction prediction applications

Applications include bacterial sRNA target discovery, miRNA–mRNA pairing, antisense oligonucleotide design, guide–target evaluation, lncRNA interaction hypotheses, viral RNA contacts, and synthetic RNA circuit design.

Genome- or transcriptome-scale target discovery requires additional sequence-windowing, multiple-testing, expression, conservation, and localization filters. A pairwise workflow is useful for mechanistic follow-up but is not by itself a whole-transcriptome target finder.

How to run RNA–RNA interaction prediction online

Open the connected workflow, review the input roles and model assumptions, then keep every method’s native result separate during comparison.

1. Define RNA roles. Label the target and query RNAs, confirm 5′-to-3′ orientation, and choose biologically relevant transcript or window boundaries.
2. Prepare sequences. Set temperature and method-specific assumptions, including whether intramolecular accessibility or full joint folding should be considered.
3. Run four methods. Run RNAcofold, RNAduplex, RNAup, and RNAplex with the same sequence pair while preserving each method’s native outputs.
4. Compare binding sites. Compare predicted contact coordinates, intermolecular structures, hybridization energies, opening-energy terms, and agreement across methods.
5. Prioritize validation. Rank candidates using the biological context and validate important interactions with expression, localization, perturbation, or direct binding evidence.

How to interpret RNA–RNA interaction prediction results

Report target and query roles, coordinates in the original transcripts, temperature, sequence boundaries, and whether the score includes accessibility. Check whether the predicted site is stable across small changes in window boundaries.

Prioritize interactions supported by multiple relevant signals: favorable interaction energy, accessible sites, conserved pairing, compatible expression and localization, and experimental perturbation. Do not equate a negative computational result with absence of interaction.

How RNA–RNA interaction prediction works

Run RNAcofold, RNAduplex, RNAup, and RNAplex in parallel to compare joint structure, duplex energy, accessibility, and fast target-screening evidence.

1. Define RNA roles. Label the target and query RNAs, confirm 5′-to-3′ orientation, and choose biologically relevant transcript or window boundaries.
2. Prepare sequences. Set temperature and method-specific assumptions, including whether intramolecular accessibility or full joint folding should be considered.
3. Run four methods. Run RNAcofold, RNAduplex, RNAup, and RNAplex with the same sequence pair while preserving each method’s native outputs.
4. Compare binding sites. Compare predicted contact coordinates, intermolecular structures, hybridization energies, opening-energy terms, and agreement across methods.
5. Prioritize validation. Rank candidates using the biological context and validate important interactions with expression, localization, perturbation, or direct binding evidence.

Inputs and outputs

Check formats before running, then inspect and download the result from every workflow step.

Inputs

• RNA inputs. FASTA TXT A target RNA and query RNA in FASTA with explicit roles, orientation, and transcript boundaries.

Outputs

• Prediction evidence. CSV JSON TXT SVG PDB mmCIF ZIP Joint folds, duplex structures, interaction coordinates, energy terms, accessibility measures, tables, and native files.