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Circular RNAs (circRNAs) act through multiple mechanisms via their sequence features to fine-tune gene expression networks. Due to overlapping sequences with linear cognates, identifying internal sequences of circRNAs remains a challenge, which hinders a comprehensive understanding of circRNA functions and mechanisms. Here, based on rolling circular reverse transcription and nanopore sequencing, we developed circFL-seq, a full-length circRNA sequencing method, to profile circRNA at the isoform level. With a customized computational pipeline to directly identify full-length sequences from rolling circular reads, we reconstructed 77,606 high-quality circRNAs from seven human cell lines and two human tissues. circFL-seq benefits from rolling circles and long-read sequencing, and the results showed more than tenfold enrichment of circRNA reads and advantages for both detection and quantification at the isoform level compared to those for short-read RNA sequencing. The concordance of the RT-qPCR and circFL-seq results for the identification of differential alternative splicing suggested wide application prospects for functional studies of internal variants in circRNAs. Moreover, the detection of fusion circRNAs at the omics scale may further expand the application of circFL-seq. Taken together, the accurate identification and quantification of full-length circRNAs make circFL-seq a potential tool for large-scale screening of functional circRNAs.

Simple Summary: The central dogma of RNA biology was traditionally governed by the transcription of DNA into messenger RNA (mRNA) followed by translation into protein. As additional functions of RNA were discovered, it became clear that there were many species of RNA, and many of those species were multifunctional with roles outside of coding for protein. One such area of discovery was the ability to form circular RNAs (circRNAs) that have been shown to be important in a wide range of cellular processes and as such are important in health and disease. These functions broadly include the modulation of gene expression, facilitation of protein–protein interactions and protein translation. It is important to understand how the dysregulation of these processes are involved in cancer progression and the avenues this may open for the targeting or overexpression of circRNAs as novel therapeutics. The majority of RNAs transcribed from the human genome have no coding capacity and are termed non-coding RNAs (ncRNAs). It is now widely accepted that ncRNAs play key roles in cell regulation and disease. Circular RNAs (circRNAs) are a form of ncRNA, characterised by a closed loop structure with roles as competing endogenous RNAs (ceRNAs), protein interactors and transcriptional regulators. Functioning as key cellular regulators, dysregulated circRNAs have a significant impact on disease progression, particularly in cancer. Evidence is emerging of specific circRNAs having oncogenic or tumour suppressive properties. The multifaceted nature of circRNA function may additionally have merit as a novel therapeutic target, either in treatment or as a novel biomarker, due to their cell-and disease-state specific expression and long-term stability. This review aims to summarise current findings on how circRNAs are dysregulated in cancer, the effects this has on disease progression, and how circRNAs may be targeted or utilised as future potential therapeutic options. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) act through multiple mechanisms via their sequence features to fine-tune gene expression networks. Due to overlapping sequences with linear cognates, identifying internal sequences of circRNAs remains a challenge, which hinders a comprehensive understanding of circRNA functions and mechanisms. Here, based on rolling circular reverse transcription and nanopore sequencing, we developed circFL-seq, a full-length circRNA sequencing method, to profile circRNA at the isoform level. With a customized computational pipeline to directly identify full-length sequences from rolling circular reads, we reconstructed 77,606 high-quality circRNAs from seven human cell lines and two human tissues. circFL-seq benefits from rolling circles and long-read sequencing, and the results showed more than tenfold enrichment of circRNA reads and advantages for both detection and quantification at the isoform level compared to those for short-read RNA sequencing. The concordance of the RT-qPCR and circFL-seq results for the identification of differential alternative splicing suggested wide application prospects for functional studies of internal variants in circRNAs. Moreover, the detection of fusion circRNAs at the omics scale may further expand the application of circFL-seq. Taken together, the accurate identification and quantification of full-length circRNAs make circFL-seq a potential tool for large-scale screening of functional circRNAs., Competing Interests: ZL, CT, SL, MD, YB, XH, YL, JC, EY No competing interests declared, (© 2021, Liu et al.)

Background: In cancer cells, fusion genes can produce linear and chimeric fusion-circular RNAs (f-circRNAs), which are functional in gene expression regulation and implicated in malignant transformation, cancer progression, and therapeutic resistance. For specific cancers, proteins encoded by fusion transcripts have been identified as innovative therapeutic targets (e.g., EML4-ALK). Even though RNA sequencing (RNA-Seq) technologies combined with existing bioinformatics approaches have enabled researchers to systematically identify fusion transcripts, specifically detecting f-circRNAs in cells remains challenging owing to their general sparsity and low abundance in cancer cells but also owing to imperfect computational methods., Results: We developed the Python-based workflow "Fcirc" to identify fusion linear and f-circRNAs from RNA-Seq data with high specificity. We applied Fcirc to 3 different types of RNA-Seq data scenarios: (i) actual synthetic spike-in RNA-Seq data, (ii) simulated RNA-Seq data, and (iii) actual cancer cell-derived RNA-Seq data. Fcirc showed significant advantages over existing methods regarding both detection accuracy (i.e., precision, recall, F-measure) and computing performance (i.e., lower runtimes)., Conclusion: Fcirc is a powerful and comprehensive Python-based pipeline to identify linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computing times compared with previously published algorithms. Fcirc empowers the research community to study the biology of fusion RNAs in cancer more effectively., (© The Author(s) 2020. Published by Oxford University Press.)