Table 1.

Detection methods of circRNAs based on sequencing data.

Method	Approach	Dataset or Database	Language	URL
Find-circ [9]	Find-circ takes two 20 bp of the reads that are not mapped and map them to the genome again. Next, the GU/AG cleavage site is found by short sequence alignment to infer the potential circular RNA sequence.	human hg19, mouse mm9, C. elegans ce6, UCSC genome browser	python	https://www.nature.com/articles/nature11928/
CIRI [37]	The fasta sequence of the input genome is compared with the same file generated by the sequencing data to detect junction reads: paired chiastic clipping signals at the junction points of the covered circular RNA. Compare junction reads. The conservative splicing sites of PEM and GT-AG are filtered, and the dynamic programming algorithm performs the final circular RNA prediction.	ENCODE RNA-seq data	perl	https://doi.org/10.1186/s13059-014-0571-3
DCC [38]	DCC uses the output from the STAR read mapper to systematically detect back-splice junctions in next-generation sequencing data. DCC applies a series of filters and integrates data across replicate sets to arrive at a precise list of circRNA candidates.	rRNA-depleted total RNA-seq data	python	https://doi.org/10.1093/bioinformatics/btv656
KNIFE [39]	Detect and quantify circular and linear RNA splicing events at the annotated and unannotated exon boundaries, including intergenic regions of the genome, thereby improving the sensitivity and specificity of circular RNA detection.	ENCODE poly(A) + and poly(A) - RNA-Seq data	Python/perl	https://link.springer.com/article/10.1186/s13059-015-0690-5
CIRC explorer [40]	Identify the linker reads from the reverse splicing exons, realign the connected reads with the existing gene annotations, determine the precise locations of the splicing sites of the downstream donor and upstream acceptor, and use a custom algorithm to adjust the mapping errors based on RefSeq exon annotations	RNase R- treated RNA-seq from H9 human embryonic stem cells (hESCs)	python	https://doi.org/10.1016/j.cell.2014.09.001
UROBORUS [41]	The artificial paired-end seed (20 bp) is first extracted from two ends of reads in an unmapped.sam file, and then aligned to the reference genome. The UROBORUS pipeline designed algorithm to deal with BMJ and UMJ reads, and detect more circRNA supported reads.	RNA-seq data of glioma samples in Hg19	perl	https://doi.org/10.1093/nar/gkw075
MAP splice [42]	In the ‘tag alignment’ phase, candidate alignments of the mRNA tags to the reference genome G are determined. In the ‘splice inference phase,’ splice junctions that appear in the alignments of one or more tags are analyzed to determine a splice significance score based on the quality and diversity of alignments that include the splice.	a synthetic noise-free RNA-seq data	python	https://doi.org/10.1093/nar/gkq622
Segemehl [43]	Segemehl is a single-end RNA-seq data segmentation read mapping algorithm, which combines seed mapping based on error ESA and fast bit vector comparison. It can accommodate multiple splits in one read and does not make a priori assumptions about the transcript structure. It is implemented in the Segemehl mapping tool, which can easily identify conventional splice junctions, collinear and non-collinear fusion transcripts, and trans-spliced RNA	RefSeq database Drosophila RNA-seq dataset human melanoma transcriptome dataset	python	https://link.springer.com/article/10.1186/gb-2014-15-2-r34
NCL scan [44]	Map RNA sequence reads with reference genomes and known transcripts, eliminate collinearity matching reads, and connect the two ends of each unmapp-ed read to generate a continuo-us sequence. Use BLAT to align each linked sequence with the reference genome. According to the corresponding BLAT comparison results and GENCODE comments, use the assumed NCL connection points to make a “hypothetical NCL reference”	hg19 /GRCh37	python	https://doi.org/10.1093/nar/gkv1013
PTES Finder [45]	PTES Finder identifies putative PTES structures by mapping RNAseq reads to sequence models generated using existing transcript annotation. It then applies a series of mapping and alignment filters to systematically remove known classes of false positives.	sample SRR4497 5A in human fibroblast total RNA	perl	http://link.springer.com/article/10.1186/s12859-016-0949-1
CircRNA-finder [46]	STAR compares the reference genome and runs circRNA-finder to get the circular RNA file whose splicing site meets the GT-AG splicing signal	Drosophila total RNA- sequencing data	perl	https://doi.org/10.1016/j.celrep.2014.10.062
CircRNAFisher [47]	CircRNAFisher is a systematic calculation pipeline suitable for whole-genome circRNA identification and annotation from scratch. CircRNAFisher combines BSJ search with a series of statistical filters to detect candidate circRNAs. It can also combine BSJ overlapping reading fragments with inconsistent BSJ Read the fragment to estimate the P value of the identified circRNA	A549 and MCF7 in RNA-Seq data from the ENCODE project; human hg19 and UCSC database	perl	https://www.nature.com/articles/s41401-018-0063-1
PcircRNA-finder [48]	PcircRNA-finder collects all backsplice sites by chiastic clipping mapping of PE reads based on available main fusion detection methods.	rRNA-/RNAase R RNA-Seq data	python	https://doi.org/10.1093/bioinformatics/btw496