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. 2020 Jan 3;11:90. doi: 10.1038/s41467-019-13840-9

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — a Workflow of circRNA quantification. CircRNAs are first identified by CIRI2 or any other computational methods. Then, CIRIquant detects back-spliced junction reads using re-alignment of RNA-seq reads against the pseudo-circular reference. Expression level and junction ratio of circRNAs are calculated using BSJ reads and FSJ reads, and stringTie is used to estimate gene abundance. b The correction of RNase R treatment efficiency. Overlapped circRNAs detected in both RiboMinus and RiboMinus/RNase R data are used for enrichment efficiency calculation and a gaussian mixture model (GMM) is used for model fitting. The fitted model is used as prior distribution for the correction of circRNA expression values. c Differential expression analysis of circRNAs. Top: For paired samples without replicates, DE-score and DS-score is directly calculated using generalized fold change. Bottom: For paired samples with biological replicates, gene expression values are used to calculate normalization factor, then the modified edgeR pipeline is used for statistical significance test.