How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• Computational prediction of RNA-RNA interactions (RRI) is crucial for understanding non-coding RNA functions.
• Existing RRI prediction methods have varying capabilities and limitations, often unclear to users.
• Accessibility-based approaches are recognized for their high reliability in RRI predictions.

Purpose of the Study:

• To review and classify seven major RRI prediction strategies.
• To discuss the advantages and limitations of different RRI prediction tools.
• To provide practical guidance on using IntaRNA, a leading accessibility-based tool, for RRI prediction tasks.

Main Methods:

• Literature review of seven classes of RRI prediction strategies.
• Analysis of the strengths and weaknesses of various RRI prediction tools.
• Demonstration of IntaRNA's application through diverse use-case examples.

Main Results:

• Accessibility-based methods, including IntaRNA, offer reliable RRI predictions.
• IntaRNA effectively handles individual and large-scale RRI prediction scenarios.
• Use-case examples with real-life data illustrate IntaRNA's flexibility and interpretation of results.

Conclusions:

• Understanding RRI prediction strategies is key to tool selection.
• IntaRNA is a versatile tool for computational RRI prediction.
• Practical, use-case driven instructions empower non-expert users to leverage IntaRNA effectively.