CircRNAs are novel members of the non-coding RNA family. For several decades 
circRNAs have been known to exist, however only recently the widespread 
abundance has become appreciated. Annotation of circRNAs depends on sequencing 
reads spanning the backsplice junction and therefore map as non-linear reads in 
the genome. Several pipelines have been developed to specifically identify these 
non-linear reads and consequently predict the landscape of circRNAs based on 
deep sequencing datasets. Here, we use common RNAseq datasets to scrutinize and 
compare the output from five different algorithms; circRNA_finder, find_circ, 
CIRCexplorer, CIRI, and MapSplice and evaluate the levels of bona fide and false 
positive circRNAs based on RNase R resistance. By this approach, we observe 
surprisingly dramatic differences between the algorithms specifically regarding 
the highly expressed circRNAs and the circRNAs derived from proximal splice 
sites. Collectively, this study emphasizes that circRNA annotation should be 
handled with care and that several algorithms should ideally be combined to 
achieve reliable predictions.