Recently circular RNA (circularRNA) has been discovered as an increasingly important type of long non-coding RNA (lncRNA), playing an important role in gene regulation, such as functioning as miRNA sponges. So it is very promising to identify circularRNA transcripts from de novo assembled transcripts obtained by high-throughput sequencing, such as RNA-seq data.
In this study, researchers from the University of Copenhagen present a machine learning approach, named as PredcircRNA, focused on distinguishing circularRNA from other lncRNAs using multiple kernel learning. Firstly they extracted different sources of discriminative features, including graph features, conservation information and sequence compositions, ALU and tandem repeats, SNP densities and open reading frames (ORFs) from transcripts. Secondly, to better integrate features from different sources, they proposed a computational approach based on a multiple kernel learning framework to fuse those heterogeneous features. Their preliminary 5-fold cross-validation result showed that our proposed method can classify circularRNA from other types of lncRNAs with an accuracy of 0.778, sensitivity of 0.781, specificity of 0.770, precision of 0.784 and MCC of 0.554 in our constructed gold-standard dataset, respectively. Their feature importance analysis based on Random Forest illustrated some discriminative features, such as conservation features and a GTAG sequence motif.
Availability – PredcircRNA tool is available for download at: https://github.com/xypan1232/PredcircRNA