Prediction of Circular RNA Secondary Structures and Their Targets.

Area of Science:

• Biochemistry
• Bioinformatics
• Molecular Biology

Background:

• Circular RNAs (circRNAs) exhibit unique structural properties distinct from linear RNAs.
• Understanding circRNA secondary structure is crucial for elucidating their biological functions.
• Existing computational tools primarily focus on linear RNA analysis.

Purpose of the Study:

• To compare the secondary structure prediction and alignment of circular RNAs (circRNAs) with linear RNAs.
• To review available software tools, particularly from the ViennaRNA package, for circRNA analysis.
• To highlight recent advancements in circRNA research, including chemical probing and interaction-based structure prediction.

Main Methods:

• Dynamic programming algorithms for RNA secondary structure prediction.
• Comparative analysis of sequence alignment algorithms for circular versus linear RNA.
• Review of computational tools within the ViennaRNA package.
• Discussion of experimental methods like chemical probing.

Main Results:

• Circular RNAs share fundamental folding principles (base pairing, stacking, loop entropy) with linear RNAs, enabling similar dynamic programming approaches.
• Pairwise and multiple sequence alignment for circRNAs is computationally more challenging than for linear RNAs.
• The ViennaRNA package offers solutions for circRNA structure analysis, though alignment tools are less developed.
• Recent developments include chemical probing applications and predicting structures of circRNA interactions.

Conclusions:

• Computational approaches for circRNA secondary structure prediction are analogous to those for linear RNAs.
• Sequence alignment remains a significant challenge for circular RNAs, with limited software solutions.
• Further development of bioinformatics tools is needed for comprehensive circRNA analysis and understanding their interactions.