CircMarker: A Fast and Accurate Algorithm for Circular RNA Detection

Abstract

While RNA is often created from linear splicing during transcription, recent studies have found that non-canonical splicing sometimes occurs. Non-canonical splicing joins 3 and 5 and forms the socalled circular RNA. It is now believed that circular RNA plays important biological roles such as affecting susceptibility in some diseases. within these few years, several experimental methods have been developed to enrich circular RNA while degrade linear RNA. Although several useful software tools for circRNA detection have been developed as well, these tools may miss many circular RNA. Also, existing tools are slow for large data because those tools often depend on reads mapping. In this paper, we present a new computational approach, named CircMarker, based on k-mers rather than reads mapping for circular RNA detection. CircMarker takes advantage of transcriptome annotation files to create k-mer table for circular RNA detection. Empirical results show that CircMarker outperforms existing tools in circular RNA detection on accuracy and efficiency in many simulated and real datasets. CircMarker can be downloaded from https://github.com/lxwgcool/CircMarker.