Exploring Methods to Detect and Analyze Full-length Circular RNAs using RNA Sequencing

TL;DR

• Standard RNA-seq protocols often overlook full-length circRNAs; isoform-level analysis requires tailored enrichment and sequencing methods.
• Total RNA-seq with rRNA depletion and RNase R treatment improves circular RNA detection, especially for low-abundance or degraded samples.
• Long-read and hybrid sequencing platforms offer the resolution needed to reconstruct complete circRNA isoforms and alternative splicing patterns.
• Tools like CIRI-full and Fcirc enable reliable detection, filtering, and annotation of circRNA structures beyond junction-level discovery.
• Biostate AI simplifies this workflow with cost-effective total RNA-seq and AI-powered analysis—ideal for labs needing accurate, full-transcript circRNA insights.

---

Introduction

Circular RNAs (circRNAs) are a distinct class of non-coding RNAs formed through back-splicing, where a downstream 5′ splice site is joined to an upstream 3′ splice site, resulting in a covalently closed loop. This structure makes circRNAs resistant to exonuclease degradation and highly stable in biological samples. Their expression is often tissue-specific and developmentally regulated, making them strong candidates for functional studies and biomarker discovery across a range of diseases.

Recent high-throughput studies have catalogued over 1.8 million human circRNA isoforms, originating from more than 880,000 unique back-splicing events. Yet despite this volume of data, most sequencing pipelines still focus on detecting a single signal, the back-splice junction, while overlooking the internal exon composition of each isoform. This limits researchers’ ability to interpret circRNA function, differentiate between isoforms, or understand their roles in splicing regulation, RNA-protein interactions, and translational potential.

This article walks through the essential experimental and computational methods for detecting and analyzing full-length circRNAs using RNA sequencing. From RNA enrichment techniques and sequencing strategy to isoform reconstruction tools, we outline what’s required to move from simple detection to meaningful, isoform-level insight.

What Are Circular RNAs

Circular RNAs (circRNAs) are a unique class of non-coding RNAs characterized by a covalently closed loop structure, lacking the 5′ cap and 3′ poly(A) tail found in linear RNAs. This circular configuration arises from a process known as back-splicing, where a downstream 5′ splice site is joined to an upstream 3′ splice site. As a result, circRNAs are more stable than their linear counterparts and resistant to exonuclease-mediated degradation.

Initially dismissed as splicing byproducts, circRNAs are now recognized for their roles in gene regulation, miRNA sponging, alternative splicing modulation, and even protein translation in certain contexts. Importantly, circRNAs are implicated in a range of biological processes and diseases, including cancer, neurological disorders, and cardiovascular conditions, making them increasingly relevant in biomarker discovery and therapeutic research.

Why Full-Length circRNA Detection Is Important

Most existing RNA sequencing studies identify circRNAs solely based on the presence of back-spliced junction (BSJ) reads, which indicate circularization but offer no information about the full sequence or isoform diversity of the circRNA. This is a critical limitation because many circRNAs have multiple possible internal exon combinations, leading to distinct isoforms from the same BSJ event.

Reconstructing the full-length sequence of circRNAs is necessary to:

• Understand their functional domains (e.g., binding sites for proteins or miRNAs)
• Characterize alternative splicing events within the circular structure
• Determine coding potential in cases where circRNAs are translated
• Differentiate functionally distinct isoforms that would otherwise be missed

Without full-length information, researchers risk oversimplifying or misinterpreting the biological role of circRNAs in cellular processes. For example, two circRNAs derived from the same gene locus could have completely different exon structures, influencing their interaction with other RNAs or proteins in the cell.

As a result, advanced sequencing techniques, especially those that support full-length transcript analysis, are now critical for accurate circRNA profiling.

While the structural and regulatory potential of circRNAs is increasingly recognized, accurately detecting and analyzing them—especially at the full-length isoform level—presents several technical and analytical challenges that standard RNA workflows often fail to address.

Challenges in circRNA Detection and Analysis

While full-length circular RNAs offer valuable insights into gene regulation and disease mechanisms, accurately detecting and characterizing them remains a significant challenge. The unique properties that make circRNAs biologically interesting, such as their circular structure and lack of polyadenylation, also complicate their identification and analysis using conventional RNA sequencing workflows.

Building on the importance of understanding full-length circRNAs, this section explores the key biological, experimental, and computational obstacles researchers face when working with them.

1. Lack of Poly(A) Tails and 5’/3′ Ends

Most RNA sequencing protocols rely on poly(A) selection to isolate mRNAs from total RNA. However, circRNAs lack polyadenylated tails and 5′ caps, making them invisible to standard mRNA-seq pipelines. Without total RNA library prep or ribosomal RNA depletion strategies, many circRNAs are simply lost during sample preparation.

2. Low Expression and Tissue-Specific Profiles

circRNAs are often expressed at lower levels than their linear counterparts and tend to show tissue-specific or developmental-stage-specific expression. This makes detection highly dependent on sample quality, sequencing depth, and experimental design. Even with high-quality RNA, circRNAs may remain undetected if not sufficiently enriched or sequenced deeply.

3. Back-Splice Junction Detection Bias

Most circRNA detection algorithms identify candidates based on back-spliced junction (BSJ) reads, which are rare and require precise alignment. These BSJs can be confused with mapping artifacts or structural rearrangements if not carefully filtered. Moreover, tools that focus only on BSJs fail to reconstruct the full exon content of the circRNA, leading to incomplete functional annotations.

4. Isoform Ambiguity and Alternative Circularization

Many circRNAs originate from the same genomic locus but differ in exon composition, forming distinct isoforms. Standard pipelines rarely reconstruct these variants in full. Without long-read sequencing or specialized algorithms, it’s difficult to distinguish between functionally distinct circRNA isoforms.

5. False Positives and Artifacts

Alignment errors, chimeric reads, or repetitive genomic regions can lead to false-positive circRNA calls. This is especially common when sequencing depth is low or quality control filters are relaxed. Computational tools must carefully account for these factors using stringent filters and proper control datasets.

6. Lack of Consensus Across Detection Tools

Different circRNA detection tools (e.g., CIRI, find_circ, circExplorer) often produce inconsistent results, especially for low-abundance transcripts or complex isoforms. This lack of agreement complicates downstream analysis and hinders reproducibility across studies.

These limitations make it clear that conventional RNA sequencing protocols—especially those built around poly(A)-based selection and short-read outputs—are not well suited for capturing circular RNAs in their full form. To address this, researchers have developed specialized experimental workflows that enrich for circRNAs and improve the accuracy of sequencing-based detection.

Experimental Methods for circRNA Enrichment and Sequencing

Detecting full-length circular RNAs (circRNAs) requires careful experimental planning. Standard RNA sequencing protocols often miss circRNAs due to their lack of poly(A) tails and low abundance. To accurately identify and reconstruct circRNA isoforms, researchers must optimize sample preparation, library construction, and sequencing platforms. This section outlines key experimental methods used in circRNA studies.

1. Ribosomal RNA Depletion

Ribosomal RNAs (rRNAs) account for more than 80–90% of total RNA. Depleting rRNA is essential to focus sequencing reads on non-ribosomal transcripts, including circRNAs. Unlike poly(A) selection, which targets linear mRNAs, rRNA depletion preserves non-polyadenylated RNAs, including circular species.

• Common kits: Ribo-Zero, NEBNext rRNA Depletion Kit
• Advantage: Retains both coding and non-coding RNAs
• Critical for total RNA sequencing workflows targeting circRNAs

2. RNase R Treatment

RNase R is a 3′ to 5′ exonuclease that selectively digests linear RNAs while sparing circular ones due to their closed-loop structure. Applying RNase R enriches the sample for circRNAs, improving detection rates, especially for low-abundance species.

• Often used prior to library preparation
• Enables higher read depth for circRNA discovery
• May not work on all circRNAs (some structured linear RNAs can resist digestion)

3. Total RNA Library Preparation

circRNAs are non-polyadenylated, so using poly(A) selection (common in mRNA-seq) will exclude them from the final library. Instead, total RNA-seq with rRNA depletion is the preferred method for capturing circular transcripts.

• Library kits should support random priming, not oligo(dT) selection
• Paired-end sequencing is preferred to help identify back-spliced junctions

4. Short-Read RNA Sequencing (Illumina)

Short-read sequencing platforms like Illumina offer high accuracy and depth, making them effective for detecting back-spliced junctions—the hallmark of circRNAs. However, due to limited read lengths (75–150 bp), they typically cannot reconstruct full-length isoforms, especially for longer circRNAs with multiple exons.

• Best for high-throughput detection of circRNA presence
• Requires computational tools to infer junctions
• Limited in resolving isoform structures

5. Long-Read Sequencing (Oxford Nanopore, PacBio)

Long-read technologies can span the entire circular transcript, providing direct evidence of full-length structure and exon composition. This is especially valuable when studying alternative circular isoforms from the same gene locus.

• Oxford Nanopore Technologies (ONT): Portable, flexible read lengths, moderate accuracy
• PacBio SMRT: Higher consensus accuracy with circular consensus sequencing (CCS)
• Supports isoform-level annotation and validation

Studies have shown that long-read sequencing reveals complex circRNA architectures that are missed by short-read data alone.

6. Hybrid Sequencing Approaches

Some researchers combine short- and long-read sequencing to take advantage of both:

• Use Illumina to detect BSJs with high confidence
• Use ONT or PacBio to resolve full-length sequences
• Hybrid pipelines like Fcirc or CIRI-long integrate both data types

This approach balances accuracy, cost, and structural resolution, especially in large-scale studies or clinical applications.

While experimental methods are essential for enriching and sequencing circular RNAs, the data generated, especially from short-read platforms, still requires advanced computational analysis to accurately identify and characterize circRNAs. The next section explores the algorithms and pipelines researchers use to detect, quantify, and analyze circular RNAs from RNA-seq data.

Computational Tools for circRNA Detection

Once RNA sequencing data is generated, whether through short-read, long-read, or hybrid approaches, the next step is computational analysis. Detecting circular RNAs in silico requires specialized algorithms that can identify back-spliced junction (BSJ) reads, distinguish true circRNAs from artifacts, and, increasingly, reconstruct full-length circRNA isoforms. Here are the key categories of tools, their methods, and their strengths and limitations.

1. Back-Splice Junction Detection Algorithms

The primary computational signature of circRNAs is the back-spliced junction, where the 3′ end of an upstream exon connects to the 5′ end of a downstream exon, opposite to canonical splicing. Most circRNA detection tools rely on mapping RNA-seq reads to a reference genome and identifying reads that align across such non-linear junctions.

Common BSJ detection tools include:

• CIRI2: Uses paired chiastic clipping signals and multiple seed matching to accurately detect BSJs. It applies stringent filtering to reduce false positives and supports paired-end data well.
• find_circ: One of the earliest tools, it uses unmapped reads from BWA and identifies BSJs using anchor-based matching. It is fast but can generate false positives due to limited filtering.
• circExplorer2: Aligns reads using STAR and then annotates BSJs while allowing for alternative splicing and multiple isoforms. Offers rich output and works well in annotation-driven studies.

2. Filtering and Validation Steps

After BSJ detection, many tools include filters to eliminate false positives, such as:

• Repetitive elements and low complexity regions
• Low read support (<2 reads per BSJ)
• Junctions near known mis-priming sites or misalignments

Additional validation may involve:

• Cross-referencing with circRNA databases (e.g., circBase, CIRCpedia)
• Checking RNase R resistance (circRNAs should persist)
• Confirming reproducibility across replicates or conditions

3. Tools for Full-Length circRNA Isoform Reconstruction

Detecting the junction alone is not sufficient for downstream functional analysis. Researchers increasingly require the entire exon composition of the circRNA. Specialized tools have emerged to reconstruct full-length circRNAs from RNA-seq data.

• CIRI-full: Combines short-read RNA-seq data with BSJ reads to infer the internal structure of circRNAs. Designed to handle multiple isoforms from the same BSJ.
• Fcirc: A hybrid approach that integrates long-read data (e.g., Nanopore) to accurately reconstruct circRNA sequences. Offers better isoform resolution than short-read-only tools.
• isocirc: Developed for Nanopore data, it identifies full-length circRNAs without relying on BSJ-specific reads. Effective in isoform discovery and annotation.

4. Visualization and Quantification Tools

• CircView and CircRNAprofiler allow researchers to visualize BSJ-supporting reads and transcript structures.
• DESeq2 or edgeR can be adapted for differential expression analysis of circRNAs, though they were originally developed for mRNAs.

While computational tools are effective at detecting back-spliced junctions and even reconstructing some full-length circRNA isoforms, their performance depends heavily on the type of sequencing data available.

Short-read platforms remain widely used for their cost and accuracy, but they are often insufficient for resolving complex circular structures. To accurately characterize exon composition and transcript length, researchers must carefully select between short-read, long-read, or hybrid sequencing strategies. The next section compares these approaches in the context of full-length circRNA analysis.

Full-length circRNA Reconstruction: Short-read vs Long-read vs Hybrid

Reconstructing full-length circular RNAs is a major analytical challenge, especially for transcripts with complex splicing patterns or multiple isoforms. The sequencing technology used directly influences the accuracy, resolution, and feasibility of circRNA isoform discovery. Below is a breakdown of how each approach contributes to or limits full-length circRNA reconstruction.

1. Short-Read RNA Sequencing

Short-read sequencing, primarily using Illumina platforms, generates high-accuracy reads ranging from 75 to 150 base pairs. These reads are ideal for detecting back-spliced junctions (BSJs) and quantifying circRNA abundance. However, they fall short in resolving the full internal structure of circRNAs.

Limitations:

• Cannot capture the full span of longer circRNAs
• Difficult to infer the exact exon composition from fragmented reads
• High dependence on computational inference to reconstruct isoforms

Use Case:

• Suitable for large-scale circRNA screens
• Effective when paired with annotation-driven pipelines like circExplorer2

Despite their limitations, short reads remain popular due to affordability, scalability, and established workflows.

2. Long-Read RNA Sequencing

Long-read platforms, such as Oxford Nanopore Technologies (ONT) and PacBio SMRT, can sequence entire RNA molecules, often exceeding 10 kb in length. This enables direct observation of the full exon composition and alternative splicing events within circular RNAs.

Advantages:

• Reads span entire circRNA isoforms without fragmentation
• Reduces reliance on inference or assembly
• Reveals novel isoforms undetectable by short-read data

Challenges:

• Higher error rates (especially ONT), though consensus calling mitigates this
• Lower throughput and higher per-sample cost
• Requires high-quality RNA input (not ideal for all clinical samples)

Use Case:

• Ideal for studies prioritizing isoform-level annotation
• Particularly effective in uncovering tissue- or disease-specific circRNA variants

3. Hybrid Sequencing Approaches

To balance cost, coverage, and resolution, many researchers now employ hybrid sequencing strategies that combine short- and long-read data.

How It Works:

• Use Illumina reads for accurate BSJ detection and quantification
• Use ONT or PacBio reads to reconstruct full-length isoforms
• Integrate data using tools like Fcirc, CIRI-long, or custom pipelines

Benefits:

• High accuracy for junction calls
• High resolution for isoform structure
• Cross-validation reduces false positives

Use Case:

• Best choice for projects requiring both transcript discovery and quantification
• Useful in clinical settings where both scale and resolution matter

Hybrid sequencing is increasingly favored in large-scale functional genomics studies, where comprehensive characterization of circRNAs is necessary to link structure with function.

Designing an Effective circRNA-Seq Experiment

Accurately detecting and reconstructing full-length circular RNAs requires more than selecting the right sequencing platform; it starts with thoughtful experimental design. From sample quality to library preparation and sequencing depth, each step influences the success of circRNA identification and downstream analysis.

This section outlines the key considerations researchers should account for when designing a circRNA-seq experiment.

1. Sample Type and Source Material

circRNAs are expressed across various tissues and cell types, but their abundance and isoform diversity can vary widely. Researchers should select tissues or samples where circRNA expression is expected or has been previously observed. Common sources include:

• Brain, heart, and liver tissue (rich in circRNAs)
• Cultured cells under stress or differentiation conditions
• Clinical samples like blood or FFPE biopsies (require special handling)

Note: FFPE samples can yield degraded RNA, so methods compatible with low RNA integrity (RIN ≥ 2) are essential.

2. RNA Quality and Integrity

RNA integrity is measured using the RNA Integrity Number (RIN). Unlike linear mRNA studies, circRNA detection is more tolerant of lower RIN values, especially when long-read platforms or total RNA protocols are used. However, high integrity (>7) is still preferred for full-length isoform reconstruction.

• Minimum input:
  • 10–100 ng RNA (short-read protocols)
  • >500 ng–1 µg RNA (long-read protocols)
• Use Bioanalyzer or TapeStation for quality assessment

3. Enrichment Strategies: RNase R vs rRNA Depletion

Choosing the right enrichment method is crucial to maximize circRNA yield:

Method	Purpose	When to Use
RNase R	Digests linear RNAs	For specific circRNA enrichment
rRNA depletion	Removes abundant rRNA	For total RNA-seq with circRNAs + other ncRNAs
Combined	RNase R + rRNA depletion	When working with low-input or FFPE samples for better signal-to-noise

Avoid poly(A) selection entirely, as it excludes non-polyadenylated circRNAs.

4. Library Preparation Parameters

circRNAs require specialized library preparation kits that:

• Support random priming, not oligo(dT)
• Retain non-poly(A) transcripts
• Allow for strand specificity, aiding in isoform analysis

Paired-end sequencing (e.g., 2 × 150 bp) is preferred in short-read studies to improve back-spliced junction detection and isoform inference.

5. Sequencing Depth and Read Design

The sequencing depth needed depends on the expected expression level of circRNAs and the platform used.

Scenario	Recommended Depth
Short-read + RNase R	≥50M paired-end reads
Short-read + total RNA	≥100M paired-end reads
Long-read (ONT or PacBio)	≥5M full-length reads
Hybrid approach	Combine both as above

Longer read lengths (e.g., 2 × 150 bp or full-length reads in ONT) improve junction resolution and full-sequence coverage.

6. Replicates and Controls

To distinguish biologically relevant circRNAs from technical noise:

• Include biological replicates (minimum 3 per condition)
• Use RNase R–treated and untreated pairs to confirm circularity
• Spike-in controls (e.g., ERCC RNA standards) can aid normalization

Include negative controls (e.g., poly(A)-selected libraries) to verify the absence of circRNAs in inappropriate protocols.

ADD

With the right experimental design in place, researchers can generate reliable circRNA datasets. The next step is translating that data into biological insight, where full-length isoform information opens doors to diverse applications in disease research, regulatory biology, and therapeutic development.

Applications of Full-Length circRNA Analysis

As research tools improve and sequencing strategies evolve, circular RNAs have moved from being molecular curiosities to promising targets in functional genomics, disease modeling, and even therapeutic development. While detection of back-spliced junctions provides a signal for the presence of circRNAs, full-length sequence information is essential for understanding their function.

Here’s how full-length circRNA analysis is applied across biomedical and clinical research:

1. Disease Biomarker Discovery

circRNAs are highly stable, often tissue-specific, and detectable in biofluids like blood and saliva. These properties make them attractive candidates for non-invasive biomarkers. However, many circRNAs share the same back-spliced junctions while differing in exon content, making isoform-level resolution crucial for specificity.

• In cancer research, distinct circRNA isoforms have been linked to tumor stage, drug resistance, and prognosis.
• In neurodegenerative diseases like Alzheimer’s and Parkinson’s, differentially expressed circRNA isoforms are being explored as early diagnostic indicators.
• Full-length sequences allow identification of disease-specific motifs, domains, or miRNA-binding regions not evident from junction-only data.

2. Regulatory Network Mapping

circRNAs often act as microRNA (miRNA) sponges, protein decoys, or scaffolds in ribonucleoprotein complexes. Understanding these roles requires knowledge of their complete sequence, including internal exons and introns.

• Full-length analysis helps identify miRNA response elements (MREs) distributed across the entire circRNA.
• Some circRNAs retain intronic sequences, which may impact nuclear retention or interaction with RNA-binding proteins (RBPs).
• Studies have used circRNA-mRNA-miRNA networks to map out gene regulatory mechanisms in various cell types.

3. Transcript Isoform Differentiation

From a single gene locus, multiple circRNA isoforms can arise via alternative back-splicing. These isoforms often differ functionally.

• Example: circZNF609 in muscle cells produces distinct isoforms that may or may not be translated.
• Without full-length reconstruction, researchers risk collapsing multiple biologically distinct circRNAs into a single annotation, leading to misinterpretation.

Isoform resolution enables better quantification, annotation, and functional hypothesis generation.

4. Translation of circRNAs into Functional Peptides

Though traditionally considered non-coding, some circRNAs contain open reading frames (ORFs) and internal ribosome entry sites (IRES) that allow them to be translated into peptides.

• Full-length sequence data is required to confirm the presence and structure of ORFs
• circRNA-derived peptides have been implicated in cancer progression and immune regulation
• Long-read circRNA analysis has uncovered novel translated circRNAs in human tissues that were previously missed in standard RNA-seq studies

This expands the functional landscape of circRNAs and supports their investigation as therapeutic targets or tools.

5. Therapeutic Design and RNA Engineering

Engineered circRNAs are being investigated as RNA therapeutics due to their stability and low immunogenicity. Synthetic circRNAs can be designed to:

• Act as miRNA sponges for therapeutic silencing
• Encode proteins with sustained expression
• Serve as decoys for pathogenic proteins

Full-length circRNA characterization helps define design parameters for therapeutic constructs, such as exon selection, circularization signals, and internal motifs.

How Biostate AI Supports Full-Length circRNA Studies

Accurately detecting and analyzing full-length circular RNAs demands more than just sequencing hardware. Researchers face multiple constraints: degraded or low-input RNA, the need for non-poly(A) protocols, and the lack of accessible tools for isoform-level reconstruction. Even when sequencing is successful, interpreting the data, especially without dedicated bioinformatics support, can limit the scientific value extracted from the experiment.

Biostate AI solves these challenges by offering an end-to-end RNA sequencing and analytics platform optimized for circRNA research. From sample prep to full-length transcript analysis, our solution is engineered to support researchers working with complex or challenging samples, without requiring in-house computational pipelines.

Biostate AI offers:

• Total RNA-Seq starting at $80 per sample: Cost-effective sequencing for academic labs and scalable projects.
• Low input volume compatibility: Process samples as small as 10µL of blood, 10ng of RNA, or a single FFPE slide.
• Low RIN acceptance (≥2): Suitable for archived or degraded samples—ideal for clinical or FFPE-derived materials.
• Full transcriptome coverage: Capture both coding and non-coding RNAs with rRNA-depleted, non-poly(A)-based protocols.
• AI-enhanced analysis via OmicsWeb: Automatically identify circular RNAs and visualize isoform structures with no coding required.
• Rapid turnaround (1–3 weeks): Accelerate experimental timelines without compromising data quality.

With Biostate AI, researchers can generate publication-ready circRNA data without the need for internal sequencing or bioinformatics infrastructure.

Conclusion

Detecting and analysing full-length circular RNAs (circRNAs) requires more than conventional RNA-seq workflows; it demands a specialized combination of experimental enrichment, sequencing strategies, and computational tools. From overcoming challenges like low expression and lack of poly(A) tails to reconstructing isoforms using long-read data, each step plays a critical role in capturing the true complexity of circRNAs.

Biostate AI streamlines this entire process by offering an end-to-end RNA sequencing and analysis platform tailored for circRNA research. With total RNA-seq workflows, low-input compatibility, AI-driven analysis, and fast turnaround times, we help researchers generate full-transcript insights without needing in-house bioinformatics resources.

Get in touch with us to start your next full-length circRNA study with Biostate AI!

FAQs

1. Can circular RNAs be detected using single-cell RNA sequencing?

Yes, but with caveats. While single-cell RNA sequencing (scRNA-seq) can theoretically detect circular RNAs, it is limited by shallow read depth and the use of poly(A)-based protocols, which typically miss non-polyadenylated transcripts like circRNAs. Newer scRNA-seq methods using total RNA and custom protocols are improving detection, but accurate full-length isoform analysis in single cells remains technically challenging.

2. What is the difference between circular RNA annotation and linear transcript annotation?

Linear transcript annotation relies on identifying start and end points (5’ and 3’ ends) along with exon-intron boundaries. Circular RNA annotation, on the other hand, must detect back-spliced junctions and reconstruct circular exon arrangements that don’t follow the linear order. Full-length circRNA annotation also requires identifying alternative splicing patterns unique to the circular form, which are not captured by standard transcriptome annotations.

3. How do you confirm that an RNA molecule is truly circular?

The most common method is RNase R treatment, which degrades linear RNAs while leaving circular RNAs intact due to their closed-loop structure. Researchers then compare treated and untreated libraries using back-spliced junction counts. Additional confirmation can involve Northern blotting, PCR with divergent primers, or long-read sequencing to verify complete circular isoforms.

4. Are there public databases for circular RNAs?

Yes, several curated databases collect circRNA sequences, annotations, and functional predictions. Examples include circBase, CIRCpedia, CircAtlas, and circRNADb. These resources provide junction coordinates, predicted miRNA binding sites, conservation scores, and experimental evidence—useful for benchmarking new findings or validating results.

5. Can circular RNAs be translated into proteins or peptides?

In some cases, yes. Certain circRNAs contain internal ribosome entry sites (IRES) or N6-methyladenosine (m6A) modifications that enable cap-independent translation. These circRNA-derived peptides have been implicated in cancer, immune regulation, and stress response pathways. However, proving translation requires experimental validation such as ribosome profiling or mass spectrometry.

While the structural and regulatory potential of circRNAs is increasingly recognized, accurately detecting and analyzing them—especially at the full-length isoform level—presents several technical and analytical challenges that standard RNA workflows often fail to address.