RNA molecules are conventionally known for the synthesis of proteins coded in the genome. However, this decade has seen an exploding number and type of RNA molecules in eukaryotic cells with the advent of next-generation sequencing. Interestingly, the noncoding (nc)RNAs contribute to more than 95% of the total RNA in the cell [
1]. The ncRNA molecules such as snRNA, snoRNA, piRNA, tRNA, rRNA, and circular RNAs (circRNAs) work in coherence to express proteins from mRNAs. Over the past few years, next-generation sequencing technologies coupled with novel bioinformatic methods have led to the identification of ubiquitously expressed closed-loop circRNA molecules [
2,
3]. CircRNAs are widely expressed in all eukaryotes, conserved, and show cell type-specific expression [
4]. Generally, the expression of the majority of circRNAs is less abundant than the counterpart linear RNAs [
3]. Although some genes produce more than ten circRNAs, most genes with circular transcripts generate one or two circRNAs [
5,
6]. Based on the circRNA sequence overlap with the parental gene, circRNAs are categorized into various types, such as exonic circRNAs (ecircRNAs), exon–intron circRNAs (EIcircRNAs), circular intronic RNAs (ciRNAs), stable intronic sequence RNAs (sisRNAs), and tRNA intronic circular RNAs (tricRNAs) [
7,
8,
9,
10]. Circularization of exonic sequences and exon–intron sequences from the precursor RNAs through backsplicing generates ecircRNAs and EIcircRNAs, respectively [
6,
11]. Unlike linear RNAs, circRNAs are generated by the head-to-tail joining of circularizing exons through backsplicing (reviewed in [
11]). Backsplicing of circRNAs from precursor mRNA requires the canonical spliceosomal machinery and the inverted intronic repeat sequences in the flanking introns of the circularizing exon [
2,
11,
12]. Additionally, several RNA-binding proteins (RBPs), including MBNL1, NF90, quacking, and DHX9, have been shown to interact with the pre-mRNA and modulate circRNA biogenesis [
11,
12,
13,
14,
15,
16]. Furthermore, the intronic sequences have been shown to generate many lariat-derived ciRNAs and sisRNAs [
9,
10]. The ciRNAs escape the debranching process due to the C-rich 11 nt motif near the branch point and the GU-rich 7 nt sequence at the 5′ splice site [
10]. TricRNAs are another class of circRNAs generated from the intron of the pre-tRNA. The bulge–helix–bulge motif of the pre-tRNA is spliced by the tRNA splicing endonuclease (TSEN) complex, followed by ligation of the intron ends by the RtcB to generate tricRNAs [
8].
Hundreds of thousands of circRNAs have been identified in humans using high-throughput sequencing coupled with various bioinformatics tools (reviewed in [
17]). However, the function of the majority of the circRNAs remains to be explored. Although much research has been performed to characterize the novel circular transcripts, no consensus has been reached to date on the biological function of these intriguing circles (reviewed in [
7]). CircRNAs lack the 5′ cap and 3′ poly-A tails, which makes them more stable compared to linear RNA, making them the right candidate for gene regulation [
3]. Increasing evidence suggests that circRNAs act as a decoy for RBPs, as a protein scaffold, as miRNA sponges, as a splicing regulator, and as a template for protein translation (reviewed in [
18]). For example, ciRNAs and EIcircRNAs have been shown to interact with RNA pol II complex and U1 snRNA to regulate the transcription of the parental genes [
10,
19]. Additionally, backsplicing generating circMBL from exon 2 of the MBL gene competes with the pre-MBL mRNA splicing, leading to alternative splicing of MBL mRNA [
13]. Since circRNA biogenesis leads to exon skipping, circRNA with the start codon of the parental mRNA can act as an mRNA trap and affect the protein expression from the parental mRNA [
20]. In addition, circRNAs have been shown to contain miRNA target sites and act as competing endogenous RNAs (ceRNAs) for miRNAs [
21]. miRNA sponging by circRNAs leads to increased expression of the cognate target mRNAs and has been extensively reviewed in [
22]. Interestingly, miRNA association with circRNA has also been shown to regulate circRNA stability [
23].
Increasing evidence suggests that circRNAs are aberrantly expressed in disease conditions and body fluids. Many recent studies have established that circRNAs may serve as a biomarker and therapeutic targets in various diseases, including cancer, diabetes, muscular atrophy, and aging (reviewed in [
24,
25,
26,
27]). CircRNAs are also increasingly shown to regulate various physiological and developmental processes by acting as a sponge for miRNAs or RBPs (reviewed in [
24,
26,
27,
28]). To understand the function of circRNA within a cell, it becomes essential to understand the expression pattern of circRNAs and their association with other biomolecules. Several computational tools have been developed to predict the association of circRNAs with cellular factors and predict their function (reviewed in [
17]). In this review, we discuss different circRNA analysis methods with a particular focus on imaging techniques.