Circular RNAs (circRNAs) are highly stable forms of non-coding RNAs with diverse 
biological functions. They are implicated in modulation of gene expression thus 
affecting various cellular and disease processes. Based on existing 
bioinformatics approaches, we developed a comprehensive workflow called Circ-Seq 
to identify and report expressed circRNAs. Circ-Seq also provides informative 
genomic annotation along circRNA fused junctions thus allowing prioritization of 
circRNA candidates. We applied Circ-Seq first to RNA-sequence data from breast 
cancer cell lines and validated one of the large circRNAs identified. Circ-Seq 
was then applied to a larger cohort of breast cancer samples (n = 885) provided 
by The Cancer Genome Atlas (TCGA), including tumors and normal-adjacent tissue 
samples. Notably, circRNA results reveal that normal-adjacent tissues in 
estrogen receptor positive (ER+) subtype have relatively higher numbers of 
circRNAs than tumor samples in TCGA. Similar phenomenon of high circRNA numbers 
were observed in normal breast-mammary tissues from the Genotype-Tissue 
Expression (GTEx) project. Finally, we observed that number of circRNAs in 
normal-adjacent samples of ER+ subtype is inversely correlated to the 
risk-of-relapse proliferation (ROR-P) score for proliferating genes, suggesting 
that circRNA frequency may be a marker for cell proliferation in breast cancer. 
The Circ-Seq workflow will function for both single and multi-threaded compute 
environments. We believe that Circ-Seq will be a valuable tool to identify 
circRNAs useful in the diagnosis and treatment of other cancers and complex 
diseases.