circular intron RNA cyclization (Stoddard, 2014); the detailed mechanisms are shown in Figure 1. The diversity of circRNAs, and as a result their diverse biological functions, is really a direct outcome of these various formation mechanisms. One example is, circRNAs can act as miRNA BRD4 Inhibitor Source sponges (Hansen et al., 2013; Memczak et al., 2013; Zhao et al., 2020a), be translated into proteins (Yang et al., 2017), bind functional proteins (Li Z. et al., 2015), regulate RNA splicing (Conn et al., 2017), and regulate transcription (Chao et al., 1998; Memczak et al., 2013). As a result, the identification of circRNAs contributes to our understanding from the formation and biological functions of circRNAs. In 1976, Kolakofsky (1976) observed, for the first time, defective interfering RNAs in parainfluenza virus particles using electron microscopy. Sanger et al. (1976) discovered that plantinfecting viroids are a class of single-stranded, circular RNA molecules that have qualities like higher thermal stability and also a organic circular structure by self-complementary. In 1979, related circular transcripts were discovered in HeLa cells and yeast mitochondria by electron microscopy (Hsu and Coca-Prados, 1979). In 1981, a ribosomal RNA (rRNA) gene was found in Tetrahymena that contained an intron sequence that formed a circular RNA immediately after splicing. In 1988, the intron of 23S rRNA in archaea was located to be spliced at a specific internet site to kind a stable circular RNA and to function as a transposon. In 1991, researchers identified many circular transcripts formed by different splicing patterns inside the human oncogene DCC (Nigro et al., 1991), and these circular RNAs have been then discovered in human ETS1 gene, mouse Sry (sex-determining region Y) gene, rat cytochrome P450 2C24 gene and human P450 2C18 gene. Regardless of their early discovery, study on circRNAs has been slow in current decades. Even though circRNAs were discovered decades ago, they couldn’t be detected by molecular techniques that relied on poly(A) enrichment because they did not have free of charge three and 5 ends. Alternatively, cyclizable exons were splicedby reverse splicing, which was different from common linear splicing. Furthermore, the mapping algorithm of early transcriptome analysis couldn’t directly map the sequenced fragments towards the genome, major for the concept that circRNAs had been byproducts of missplicing. Using the development of high-throughput sequencing and bioinformatics technologies, it was very first proposed in 2012 that circRNAs are circular transcripts generated by reverse splicing of mRNA precursors, that are located to exist in massive quantities in different varieties of human cells. In 2013, it was found that circRNAs can act as a sponge for miRNAs (Hansen et al., 2013; Memczak et al., 2013), which regulate the growth and improvement of organisms. Because then, circRNAs have quickly grow to be a investigation hotspot. To recognize circRNAs, in addition to high-throughput tactics (RNA-seq), common analytical and computational solutions are applied, such as CIRI (Gao et al., 2015), segemehl (Hoffmann et al., 2014), Mapsplice (Wang et al., 2010), and CircSeq (Guo et al., 2014). In recent years, researchers have created machine learning procedures to recognize circRNAs according to the above strategies (Yin et al., 2021). Function choice is definitely an vital part of these machine learning models. Function choice, aiming to pick a subset of Dopamine Receptor Modulator Accession capabilities by eliminating redundant and noise options, is an crucial preprocessing step in bioinformatics. Lately, S.
