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

By Yuan Gao, Jinfeng Wang, Fangqing Zhao. Genome Biology, 2015

978 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ee557a7f8fbba89deeaa8795bacdaed5a2da483f

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Citations in the corpus

CircRNA: functions and properties of a novel potential biomarker for cancer (2017, background): “It contains two intronic circRNA fragments flanked by GT-AC splicing signals acting as the splice donor and acceptor of the circular junction while forming an integrated circRNA [24].”
Comprehensive characterization of tissue-specific circular RNAs in the human and mouse genomes (2016, methodology): “According to previous research, we applied three algorithms, CIRI [7, 33], circRNA_finder [34] and find_circ [6], which is the combination with the most efficiency and accuracy [35] to detect the back-splice junction sites of circRNAs.”
Detecting circular RNAs: bioinformatic and experimental challenges (2016, methodology): “In addition, there can be a high variability in results between RNase R-treated replicates, with fewer than 50% of the circ-RNAs that are resistant in one replicate also resistant in the second replicate prepared by the same laboratory 3,27 .”
Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal development (2015, methodology): “As reported in [23], find_circ detected fewer circles than CIRI in all samples tested.”
CircNet: a database of circular RNAs derived from transcriptome sequencing data (2015, background): “(20), we took the full flanked sequences into consideration for expression and miRNA target search.”
CircAtlas: an integrated resource of one million highly accurate circular RNAs from 1070 vertebrate transcriptomes (2020, methodology): “Briefly, the circRNAs in each species were identified using four detection algorithms, including CIRI2 [26, 27], find_circ [6], CIRCexplorer2 [28], and DCC [29], which have been widely used and extensively tested by previous studies [1, 30 – 32].”
Specific identification and quantification of circular RNAs from sequencing data (2016, methodology): “%) than the one of CIRI and KNIFE with 89% and 41%, respectively.”
CSCD: a database for cancer-specific circular RNAs (2017, methodology): “Identification of potential splicing exons of circRNA was performed by CIRI2 (31).”
Comparison of circular RNA prediction tools (2015, methodology): “…of two non-treated RNA samples derived from Hs68 fibroblast (SRR444655 and SRR444975) and two RNase R treated samples (SRR444974 and SRR445016), we compared five different circRNA predicting algorithms: find circ (7), MapSplice (17), CIRCexplorer (10), cir-cRNA finder (11), and CIRI (12).”