CIRI: an efficient and unbiased algorithm for de novo circular RNA identification

• Published in: Genome Biology Vol. 16; no. 1; p. 4
• Main Authors: Gao, Yuan, Wang, Jinfeng, Zhao, Fangqing
• Format: Journal Article
• Language: English
• Published: England BioMed Central 13.01.2015; BioMed Central Ltd
• Subjects: Acids; Algorithms; Annotations; Base Sequence; biogenesis; Bioinformatics; Candidates; Circular RNA; class; Computer Simulation; Databases, Nucleic Acid; Datasets; Exons - genetics; Exoribonucleases - metabolism; filtration; Gene expression; Gene Expression Regulation; Genes; genome; Genomes; HEK293 Cells; HeLa Cells; Humans; Identification; Introns - genetics; Method; MicroRNAs; Molecular Sequence Data; non-coding RNA; Reproducibility of Results; Ribonucleic acid; RNA; RNA - genetics; sequence analysis; Sequence Analysis, RNA; transcriptome; Transcriptome - genetics
• ISSN: 1465-6906; 1474-7596; 1474-760X; 1465-6906; 1474-760X; 1465-6914
• DOI: 10.1186/s13059-014-0571-3

Recent studies reveal that circular RNAs (circRNAs) are a novel class of abundant, stable and ubiquitous noncoding RNA molecules in animals. Comprehensive detection of circRNAs from high-throughput transcriptome data is an initial and crucial step to study their biogenesis and function. Here, we present a novel chiastic clipping signal-based algorithm, CIRI, to unbiasedly and accurately detect circRNAs from transcriptome data by employing multiple filtration strategies. By applying CIRI to ENCODE RNA-seq data, we for the first time identify and experimentally validate the prevalence of intronic/intergenic circRNAs as well as fragments specific to them in the human transcriptome.