A decade ago, the first bioinformatics pipelines to detect circular RNA molecules based on short-read sequencing data were published. Here, we show that dozens of such circular RNA detection tools differ vastly in their sensitivity but not in their specificity.
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Salzman, J., Gawad, C., Wang, P. L., Lacayo, N. & Brown, P. O. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS ONE 7, e30733 (2012). This paper reports large-scale circRNA formation from many human genes.
Jeck, W. et al. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19, 141–157 (2013). This paper confirms the findings of Salzman et al. and describes circRNA features.
Memczak, S. et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495, 333–338 (2013). This paper uses a circRNA detection tool, find_circ, and discusses the tissue- and developmental-stage-specific expression of circRNAs, with CDR1as as a case study.
Vromman, M. et al. Validation of circular RNAs using RT-qPCR after effective removal of linear RNAs by ribonuclease R. Curr. Protoc. 1, e181 (2021). This protocol was used for the validation of more than a thousand circRNAs in the benchmarking study.
Chen, L. L. et al. A guide to naming eukaryotic circular RNAs. Nat. Cell Biol. 25, 1–5 (2023). This paper proposes a uniform approach to circRNA nomenclature.
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This is a summary of: Vromman, M. et al. Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision. Nat. Methods https://doi.org/10.1038/s41592-023-01944-6 (2023).
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Circular RNA detection pipelines yield divergent sets of circular RNAs. Nat Methods 20, 1135–1136 (2023). https://doi.org/10.1038/s41592-023-01945-5