Institute of Systems Biomedicine, Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission of China , NHC Key Laboratory of Medical Immunology (Peking University), Beijing, China
Changyu Tao
Department of Human Anatomy, Histology & Embryology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
Shiwei Li
Department of Radiation Medicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
Minghao Du
Department of Microbiology & Infectious Disease Center, School of Basic Medical Science Peking University Health Science Center, Beijing, China
Yongtai Bai
Department of Radiation Medicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
Xueyan Hu
Institute of Systems Biomedicine, Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission of China , NHC Key Laboratory of Medical Immunology (Peking University), Beijing, China
Yu Li
Chinese Institute for Brain Research, Beijing, China
Jian Chen
Chinese Institute for Brain Research, Beijing, China
Institute of Systems Biomedicine, Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Key Laboratory for Neuroscience, Ministry of Education/National Health Commission of China , NHC Key Laboratory of Medical Immunology (Peking University), Beijing, China; Department of Microbiology & Infectious Disease Center, School of Basic Medical Science Peking University Health Science Center, Beijing, China; Chinese Institute for Brain Research, Beijing, China
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.