RNA-seq analysis and identification of circRNAs

RNA-seq was performed using an Illumina HiSeqTM 2500. The data were deposited in the SRA database [PRJNA355185 (SRP095744)]. The short reads alignment tool Bowtie2 was used for mapping reads to the ribosome RNA (rRNA) database. The rRNA mapped reads were removed. The remaining reads were further used in alignment and analysis. The removed rRNA reads of each sample were then mapped to a reference genome byTopHat2 (version 2.0.3.12). The reads that could be mapped to the genome were discarded, and the unmapped reads were then collected for circRNA identification. 20mers from both ends of the unmapped reads were extracted and aligned to the reference genome to identify unique anchor positions within the splice site. Anchor reads that aligned in the reverse orientation (head-to-tail) indicated circRNA splicing and were then subjected to find_circ to identify circRNAs. The anchor alignments were then extended such that the complete read aligns and the breakpoints were flanked by GU/AG splice sites. A candidate circRNA was called if it was supported by at least two unique back spliced reads in at least one sample. circRNAs were blasted in the circBase for annotation. Those sequences that could not be annotated were defined as novel circRNAs. CIRIquant software was used for accurate quantification of circRNAs. To identify differentially expressed circRNAs across samples or groups, the edge R package (http://www.r-project.org/) was used. We identified circRNAs with a fold change ≥ 2 and a P value < 0.05 in a comparison between samples or groups as significantly differentially expressed circRNAs.