Abstract
Circular RNAs (circRNAs) are endogenous RNAs with a covalently closed continuous loop, generated through various backsplicing events of pre-mRNA. An accumulating number of studies have shown that circRNAs are potential biomarkers for major human diseases such as cancer and Alzheimer's disease. Thus, identification and prediction of human disease-associated circRNAs are of significant importance. To this end, a computational analysis-assisted strategy is indispensable to detect, verify, and quantify circRNAs for downstream applications. In this review, we briefly introduce the biology of circRNAs, including the biogenesis, characteristics, and biological functions. In addition, we outline about 30 recent bioinformatic analysis tools that are publicly available for circRNA study. Principles for applying these computational strategies and considerations will be briefly discussed. Lastly, we give a complete survey on more than 20 key computational databases that are frequently used. To our knowledge, this is the most complete and updated summary on publicly available circRNA resources. In conclusion, this review summarizes key aspects of circRNA biology and outlines key computational strategies that will facilitate the genome-wide identification and prediction of circRNAs.
Highlights
Circular RNAs are traditionally viewed as noncoding RNAs that form a covalently closed continuous loop and thought to be generated from imperfect splicing
The generation of circRNAs from such noncanonical RNA splicing appears to be a feature of human gene expression [1]
Given the importance of circRNAs in gene expression regulation, a growing interest emerges in identifying novel circRNAs and understanding their biological functions
Summary
Circular RNAs (circRNAs) are traditionally viewed as noncoding RNAs that form a covalently closed continuous loop and thought to be generated from imperfect splicing. The generation of circRNAs from such noncanonical RNA splicing appears to be a feature of human gene expression [1]. In 2012, using deep RNA sequencing (RNA-seq) of normal and cancer stem cells from human samples, circRNAs were identified from a substantial fraction of spliced precursor message RNAs (pre-mRNAs) that showed a noncanonical order [1], suggesting a new feature of the gene expression program in human cells. Treating RNAs with RNA exonuclease to deplete linear RNAs, researchers were able to perform bioinformatic analysis to identify complementary ALU repeats in introns; the results showed that circRNAs are abundant and stable RNA splicing products and are not randomly produced, suggesting that circRNAs are truly involved in gene expression regulation [31]. It is worth noting that all these discoveries would not have been made possible without the advancement of HTS technology
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