Fig. 2

Performance evaluation of the RO approach to circRNA identification. a, b Performance comparison between the RO approach and the BSJ-based tools. “RO only” represents the circRNAs that are only identified by the RO approach. a CircRNA detection rate on the four data sets with different circRNA depth. b CircRNA detection rate on the four data sets with different read length. c Component of circular RNA and circular RNA reads detect by RO in simulated data (5X, paired-end 200 bp). d Base depth distribution of BSJ reads (pink) and RO reads (green) on normalized circular RNAs. e, f Accuracy evaluation of the AS events-based and the FSG-based quantification algorithms (CIRI-AS vs. CIRI-full) using simulated circRNA-containing transcriptomic data sets, including different sequencing depth (e) and different read length (f). g Sensitivity evaluation of the FSG-based quantification algorithm on simulated circRNA-containing transcriptomic data sets, where each circRNA contains three isoforms with different abundance. The bar plot on the right top displays the number of isoforms detected in 994 circRNAs; the bar plot on the right bottom shows the accuracy of FSG quantification in three types of isoforms. Accuracy rate is defined as the percentage of isoforms that are fully reconstructed and of which the predicted relative abundance matches the ground truth (difference between them is smaller than 20%). h, i The accuracy distribution of FSG method on the three types of reconstructed isoforms. j Experimental validation of the FSG-based isoform quantification algorithm. X- and y-axis represent the relative abundance of circRNA isoforms determined by qPCR and the FSG-based algorithm, respectively. Each dot represents a circRNA isoform, and dots in the same color represents that they come from the same circRNA