RT Journal Article
SR Electronic
T1 CircMarker: A Fast and Accurate Algorithm for Circular RNA Detection
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 134411
DO 10.1101/134411
A1 Xin Li
A1 Chong Chu
A1 Jingwen Pei
A1 Ion Măndoiu
A1 Yufeng Wu
YR 2017
UL http://biorxiv.org/content/early/2017/05/05/134411.abstract
AB 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.