Recent advances in the detection and functional analysis of circRNAs with short-read RNA sequencing-based methods
Clinical Snapshot
PICO Framework
| P — Population | Not applicable in the traditional clinical sense; the subject population encompasses circRNA research studies, bioinformatics tools, and experimental datasets derived from human and animal biological specimens across cancer, neurodegenerative, and cardiovascular disease contexts |
| I — Intervention | Short-read RNA sequencing (RNA-seq)-based bioinformatics tools and pipelines for circRNA detection, consensus calling, annotation, and protein-coding potential prediction (including backsplice junction identification, multi-tool consensus strategies, and machine/deep learning approaches) |
| C — Comparator | Earlier-generation or single-tool circRNA detection methods; manual split-read inspection; long-read sequencing approaches (referenced implicitly as alternatives) |
| O — Outcomes | Sensitivity and specificity of circRNA detection tools; generation of high-confidence circRNA datasets; identification of circRNA genomic origins and structural characteristics; prediction of protein-coding potential via ORFs, IRES elements, cap-independent translation, and N6-methyladenosine modifications |
Bottom Line
This narrative review from UNSW Sydney provides a timely and comprehensive synthesis of short-read RNA-seq bioinformatic tools for circular RNA (circRNA) detection, annotation, and protein-coding potential prediction, with a focus on advances from the past five years. circRNAs — covalently closed, exonuclease-resistant RNA molecules generated through backsplicing — have emerged as promising biomarkers and functional regulators in cancer, neurodegeneration, and cardiovascular disease. The review's most clinically actionable finding is the recommendation for multi-tool consensus calling to mitigate the considerable variability in sensitivity and specificity among individual circRNA detection algorithms. The inclusion of emerging machine learning and deep learning tools for protein-coding potential prediction reflects the field's rapid evolution. However, the absence of a formal systematic search strategy, independent benchmarking, and quality assessment of primary studies limits the evidentiary rigour of the synthesis. A bibliographic inconsistency between the listed journal and DOI warrants verification. For Australian clinicians and researchers, circRNA diagnostics remain in the discovery and validation phase — no PBS-listed or TGA-approved circRNA biomarker assay currently exists. This review is best regarded as a high-quality methodological resource for research groups establishing or optimising RNA-seq pipelines, rather than a basis for immediate clinical practice change.
Key Findings
P Value: Not reported — narrative synthesis only
Effect Size: Not applicable — narrative review; no pooled effect size calculated. Individual tool sensitivity and specificity vary considerably across benchmarking studies, supporting the authors' recommendation for multi-tool consensus approaches
Primary Outcome: Synthesis of short-read RNA-seq bioinformatic tools for circRNA detection, consensus calling, genomic annotation, and protein-coding potential prediction, with emphasis on advances from the past five years
Nnt Or Sensitivity: Not quantitatively pooled; the review notes that individual circRNA callers vary considerably in sensitivity and specificity, and that combined tool usage generates higher-confidence datasets. Specific sensitivity/specificity figures are drawn from primary benchmarking studies and not aggregated
Confidence Interval: Not reported — no meta-analytic pooling performed
Clinical Application
The bioinformatic tools reviewed are predominantly open-source and computationally accessible to research groups with standard high-performance computing infrastructure. The multi-tool consensus approach recommended by the authors requires familiarity with command-line bioinformatics but is feasible in well-resourced academic and clinical genomics settings. Implementation in routine clinical diagnostics remains premature pending prospective validation of specific circRNA biomarkers This review is directly relevant to the Australian research context, authored from UNSW Sydney. Australian research groups funded through NHMRC and ARC working in cancer genomics (including through AGHA — Australian Genomics Health Alliance), neurodegeneration, and cardiovascular disease can apply these methodologies to existing biobank RNA-seq datasets. circRNA biomarkers are not currently listed on the PBS or approved by the TGA for clinical diagnostic use. RACGP guidelines do not yet address circRNA-based diagnostics. The review supports the foundational research infrastructure needed before any regulatory pathway could be considered. Australian clinical laboratories considering RNA-seq panels for oncology or rare disease should be aware of circRNA detection as an emerging analytical layer within existing workflows Biomedical researchers, molecular biologists, and clinical genomics laboratories working with RNA-seq data in the context of cancer, neurodegenerative disorders (including Alzheimer's and Parkinson's disease), and cardiovascular disease. Translational researchers seeking to identify circRNA biomarkers or therapeutic targets from existing or prospective RNA-seq datasets
Abstract
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.
References
- 1.Lindner, G., Xu, S.-M., Santucci, K., Gao, Y., & Janitz, M. (2026). Recent advances in the detection and functional analysis of circRNAs with short-read RNA sequencing-based methods. Molecular Biology Reports. https://doi.org/10.1016/j.ymeth.2021.09.010
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