cirCodAn

cirCodAn predicts coding regions in circular RNAs (circRNAs) to identify open reading frames (ORFs) and assess their translational potential.

Key Features:

• GHMM-Based Prediction: Utilizes a Generalized Hidden Markov Model (GHMM) to model circRNA sequence structure and predict coding regions.
• ORF Identification in Circular Sequences: Identifies open reading frames (ORFs) specifically within non-linear circular RNA sequences.
• Evaluation on Translation-Evidence Datasets: Developed and evaluated using datasets comprising circRNAs with strong evidence of translation.
• Comparative Performance: Demonstrated superior performance compared to existing tools that attempt ORF prediction in circRNAs.
• Research Applicability: Provides outputs intended to support studies of circRNA-encoded proteins and their roles in biological processes.

Scientific Applications:

• Translational potential analysis: Enables identification of circRNAs that are likely to be translated into peptides or proteins.
• Peptide/protein discovery: Supports discovery of novel peptides and proteins encoded by circRNAs for downstream characterization.
• Disease and development studies: Facilitates investigation of circRNA contributions to disease mechanisms and developmental processes.

Methodology:

Implements a Generalized Hidden Markov Model (GHMM) to detect ORFs within circular RNA sequences and was developed and evaluated on circRNA datasets with evidence of translation.