cirCodAn: A GHMM-based tool for accurate prediction of coding regions in circRNA

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Circular RNAs (circRNAs) are increasingly recognized for their roles in biological processes and disease.
• Characterizing circRNAs with translation potential is crucial for understanding their functions.
• Existing tools for open reading frame detection are ineffective for circular sequences.

Purpose of the Study:

• To introduce cirCodAn, a novel computational tool for predicting coding regions in circRNAs.
• To address the limitations of current tools in detecting open reading frames in circular sequences.

Main Methods:

• Development of the cirCodAn tool for circRNA open reading frame prediction.
• Evaluation of cirCodAn's performance using circRNA datasets with experimental translation evidence.

Main Results:

• cirCodAn demonstrated superior accuracy in detecting coding regions within circRNAs compared to existing tools.
• The tool successfully identified potential coding regions in circRNAs with strong translation evidence.

Conclusions:

• cirCodAn is a valuable tool for identifying coding potential in circRNAs.
• This tool will facilitate future research into the biological roles of circRNAs and their encoded peptides.
• cirCodAn is freely available for use in scientific research.