Accurate quantification of circular RNAs identifies extensive circular isoform switching events

Jinyang Zhang,Jingwen Yang,Fangqing Zhao,Shuai Chen

doi:10.1038/s41467-019-13840-9

Abstract

Detection and quantification of circular RNAs (circRNAs) face several significant challenges, including high false discovery rate, uneven rRNA depletion and RNase R treatment efficiency, and underestimation of back-spliced junction reads. Here, we propose a novel algorithm, CIRIquant, for accurate circRNA quantification and differential expression analysis. By constructing pseudo-circular reference for re-alignment of RNA-seq reads and employing sophisticated statistical models to correct RNase R treatment biases, CIRIquant can provide more accurate expression values for circRNAs with significantly reduced false discovery rate. We further develop a one-stop differential expression analysis pipeline implementing two independent measures, which helps unveil the regulation of competitive splicing between circRNAs and their linear counterparts. We apply CIRIquant to RNA-seq datasets of hepatocellular carcinoma, and characterize two important groups of linear-circular switching and circular transcript usage switching events, which demonstrate the promising ability to explore extensive transcriptomic changes in liver tumorigenesis.

Highlights

Detection and quantification of circular RNAs face several significant challenges, including high false discovery rate, uneven ribosomal RNA (rRNA) depletion and RNase R treatment efficiency, and underestimation of back-spliced junction reads
To rigorously evaluate the challenges in current quantification of circRNA expression, we collected 63 transcriptomic samples from six previous studies[16,17,18,19,20,21,22], including both RiboMinus and RiboMinus/RNase R RNA-seq libraries of four species. All these RNA-seq datasets were aligned to their reference genomes and the ribosomal RNA sequences using HISAT223 to assess the mapping rate and rRNA sequence fraction
Several computational methods have been developed for circRNA detection, the reliability of the output is a crucial factor for further analysis

Summary

Introduction

Detection and quantification of circular RNAs (circRNAs) face several significant challenges, including high false discovery rate, uneven rRNA depletion and RNase R treatment efficiency, and underestimation of back-spliced junction reads. By constructing pseudo-circular reference for re-alignment of RNA-seq reads and employing sophisticated statistical models to correct RNase R treatment biases, CIRIquant can provide more accurate expression values for circRNAs with significantly reduced false discovery rate. A modelbased strategy is employed by Sailfish-cir[10], which uses a quasimapping method to acquire direct estimation of circular transcript expression. This statistical model depends on the unique sequence between circular and linear transcripts, which limits its ability on quantifying exonic circRNAs. the ratio of BSJ reads to canonical linear reads at the junction, which represents the splicing preference in precursor mRNAs, is an important factor in circRNA analysis. A reliable computational tool is urgently needed for accurate quantification of circRNAs and their parental linear transcripts

Methods

Results

Conclusion