Identification of Chrysanthemum stunt viroid (CSVd) circular RNA replicons from dsRNA-seq transcriptome data
This study aims to identify circular RNA replicons from double-stranded RNA sequencing (dsRNA-seq) data using a workflow that does not rely on sequence similarity. As a model system, we analyzed full-length dsRNA-seq (FLDS) data from Chrysanthemum leaves infected with Chrysanthemum stunt viroid (CSVd). Clean reads were assembled de novo, and candidate circular RNAs were predicted based on terminal redundancy. Filtering with similarity searches, coverage statistics, and secondary structure prediction enabled the identification of CSVd variants with high sequence identity to the reference. The resulting dataset contributes to understanding structure-dependent detection of circular RNAs in dsRNA-seq data.