INTEGRATE-Circ and INTEGRATE-Vis: unbiased detection and visualization of fusion-derived circular RNA.

Abstract

Motivation: Backsplicing of RNA results in circularized rather than linear transcripts, known as circular RNA (circRNA). A recently discovered and poorly understood subset of circRNAs that are composed of multiple genes, termed fusion-derived circular RNAs (fcircRNAs), represent a class of potential biomarkers shown to have oncogenic potential. Detection of fcircRNAs eludes existing analytical tools, making it difficult to more comprehensively assess their prevalence and function. Improved detection methods may lead to additional biological and clinical insights related to fcircRNAs.

Results: We developed the first unbiased tool for detecting fcircRNAs (INTEGRATE-Circ) and visualizing fcircRNAs (INTEGRATE-Vis) from RNA-Seq data. We found that INTEGRATE-Circ was more sensitive, precise and accurate than other tools based on our analysis of simulated RNA-Seq data and our tool was able to outperform other tools in an analysis of public lymphoblast cell line data. Finally, we were able to validate in vitro three novel fcircRNAs detected by INTEGRATE-Circ in a well-characterized breast cancer cell line.

Availability and implementation: Open source code for INTEGRATE-Circ and INTEGRATE-Vis is available at https://www.github.com/ChrisMaherLab/INTEGRATE-CIRC and https://www.github.com/ChrisMaherLab/INTEGRATE-Vis.