Abstract
Circular RNAs (circRNAs) are transcript isoforms generated by back-splicing of exons and circularisation of the transcript. Recent genome-wide maps created for circular RNAs in humans and other model organisms have motivated us to explore the repertoire of circular RNAs in zebrafish, a popular model organism. We generated RNA-seq data for five major zebrafish tissues - Blood, Brain, Heart, Gills and Muscle. The repertoire RNA sequence reads left over after reference mapping to linear transcripts were used to identify unique back-spliced exons utilizing a split-mapping algorithm. Our analysis revealed 3,428 novel circRNAs in zebrafish. Further in-depth analysis suggested that majority of the circRNAs were derived from previously well-annotated protein-coding and long noncoding RNA gene loci. In addition, many of the circular RNAs showed extensive tissue specificity. We independently validated a subset of circRNAs using polymerase chain reaction (PCR) and divergent set of primers. Expression analysis using quantitative real time PCR recapitulate selected tissue specificity in the candidates studied. This study provides a comprehensive genome-wide map of circular RNAs in zebrafish tissues.
Highlights
The recent years have seen an in-depth annotation of transcriptome using next-generation sequencing technologies, significantly enhancing the repertoire of transcripts and transcript isoforms identified for protein-coding as well as noncoding RNA genes[1]
In order to validate the origin of the back-spliced transcripts from the transcriptome, and not from the genome, polymerase chain reaction (PCR) amplification was performed on complementary DNA (cDNA) of corresponding tissues and genomic DNA (gDNA) of ASWT zebrafish, simultaneously, using divergent primers
We used an algorithm reported by Memczak and co-workers, previously used to characterize the human circular RNA compendium[2]
Summary
The recent years have seen an in-depth annotation of transcriptome using next-generation sequencing technologies, significantly enhancing the repertoire of transcripts and transcript isoforms identified for protein-coding as well as noncoding RNA genes[1]. BWA-MEM algorithm was used to generate SAM file for each tissue RNA-seq reads and the files were further processed through the CIRI pipeline to identify circRNA junctions. In order to validate the origin of the back-spliced transcripts from the transcriptome, and not from the genome, PCR amplification was performed on cDNAs of corresponding tissues and gDNA of ASWT zebrafish, simultaneously, using divergent primers.
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