Amplification Optimized and Unique Molecular Identifier Guided High Accuracy Full-length CircRNA Sequencing

As an emerging important regulatory noncoding RNA, circular RNAs (circRNAs) present significant spatiotemporal expression patterns in a variety of physiological processes and diseases. Thus, accurate identification and quantification of circRNA is crucial to understanding its functions and clinical significance. However, obvious inconsistencies exist between mainstream high-throughput circRNA identification workflows based on next-generation sequencing and third-generation sequencing technologies, likely due to uncertainties inherent to each workflow. In the current study, we first confirmed that sequencing error introduced in the library preparation is a considerable contributor to the observed inconsistencies. To assess this challenge, we established a UMI-based full-length circRNA sequencing method, ucircFL-seq. By employing UMI and optimizing signal amplification procedures, ucircFL-seq achieved a substantial improvement in the accuracy of both circRNA detection and quantification, leading to stronger cross-platform concordance. Furthermore, our study revealed that the two platforms identify distinct pools of circRNAs, which exhibited differences in length and secondary structure, suggesting the complementary nature of the two platforms in circRNA identification. Overall, our study presents a UMI-guided workflow, ucircFL-seq, which enhances full-length circRNA identification and quantification accuracy, facilitating further functional exploration of circRNAs.