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Polyadenylated RNA sequencing analysis helps establish a reliable catalog of circular RNAs - a bovine example.

Annie RobiciD, Frieder HadlichiD, Gabriel Costa Monteiro MoreiraiD, Emily L. ClarkiD, Graham Stuart PlastowiD, Carole CharlieriD, Christa KuehniD

Abstract

The aim of this study was to compare the circular transcriptome of divergent tissues in order to understand: i) the presence of circular RNAs (circRNAs) that are not exonic circRNAs, i.e. originated from backsplicing involving known exons and, ii) the origin of artificial circRNA (artif_circRNA), i.e. circRNA not generated in-vivo. CircRNA identification is mostly an in-silico process, and the analysis of data from the BovReg project (https://www.bovreg.eu/) provided an opportunity to explore new ways to identify reliable circRNAs. By considering 117 tissue samples, we characterized 23,926 exonic circRNAs, 337 circRNAs from 273 introns (191 ciRNAs, 146 intron circles), 108 circRNAs from small non-coding genes and nearly 36.6K circRNAs classified as other_circRNAs. We suggested in-vivo copying of specific exonic circRNAs by an RNA-dependent RNA polymerase (RdRP) to explain the 20 identified circRNAs with reverse-complement exons. Furthermore, for 63 of those samples we analyzed in parallel data from total-RNAseq (ribosomal RNAs depleted prior to library preparation) with paired mRNAseq (library prepared with poly(A)-selected RNAs). The high number of circRNAs detected in mRNAseq, and the significant number of novel circRNAs, mainly other_circRNAs, led us to consider all circRNAs detected in mRNAseq as artificial. This study provided evidence that there were 189 false entries in the list of exonic circRNAs: 103 artif_circRNAs identified through comparison of total-RNAseq/mRNAseq using two circRNA tools, 26 probable artif_circRNAs, and 65 identified through deep annotation analysis. This study demonstrates the effectiveness of a panel of highly expressed exonic circRNAs (5-8%) in analyzing the diversity of the bovine circular transcriptome.

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02 /Specification & Classification

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