Recent advances in the detection and functional analysis of circRNAs with short-read RNA sequencing-based methods - Grace Lindner; Si-Mei Xu; Kristina Santucci; Yulan Gao; Michael Janitz (Molecular Biology Reports, 2026)
Circular RNAs (circRNAs) were first identified approximately 50 years ago in pathogenic viroids as single-stranded, covalently closed RNA molecules. Initially considered by-products of splicing, circRNAs are now recognised as an important class of regulatory RNAs involved in microRNA sponging, RNA–protein interactions, and cellular pathways. Their closed-loop structure, generated through backsplicing, confers resistance to exonucleolytic degradation and contributes to their stability. Owing to their tissue- and disease-specific expression, circRNAs have emerged as promising biomarkers for cancer, neurodegenerative disorders, and cardiovascular disease. Over the past decade, numerous bioinformatics tools utilising RNA-sequencing (RNA-seq) data have been developed for circRNA detection and analysis. Detection methods have evolved from manual split-read inspection to automated identification of the back spliced junction, while annotation pipelines now resolve the genomic origins and structural characteristics of circRNAs. Because individual circRNA callers vary considerably in sensitivity and specificity, a combined usage of tools in circRNA detection has become the preferred strategy for generating high-confidence datasets. Beyond their non-coding functions, increasing evidence suggests that some circRNAs possess protein-coding potential through open reading frames, cap-independent translation mechanisms, internal ribosome entry sites (IRESs), and N6-methyladenosine modifications. A new generation of bioinformatic tools can now assess the protein-coding potential of circRNAs, integrating the above features, as well as machine learning and deep learning approaches refining these predictions. This review summarises recently developed short-read RNA-seq bioinformatics tools for circRNA detection, consensus calling, annotation, and protein-coding potential prediction, with a particular focus on advances from the past five years that facilitate the identification of translatable circRNAs.