Supporting data for "Fcirc: A Comprehensive Pipeline for Exploration of Fusion Linear and Circular RNAs"

In cancer cells, fusion genes can produce linear- and chimeric fusion-circular ribonucleic acids (f-circRNAs) which are functional in gene expression regulation and implicated in malignant transformation, cancer progression and therapeutic resistance. For specific cancers, proteins encoded by fusion transcripts have been identified as innovative therapeutic targets (e.g. EML4-ALK). Even though RNA sequencing (RNA-Seq) technologies combined with existing bioinformatics approaches have enabled researchers to systematically identify fusion transcripts, specifically detecting f-circRNAs in cells remains challenging due to their general sparsity, low abundance in cancer cells but also due to imperfect computational methods. We developed the python-based workflow Fcirc to identify fusion linear and f-circRNAs from RNA-Seq data with high specificity. We applied Fcirc to three different types of RNA-Seq data scenarios: 1) actual synthetic spike-in RNA-Seq data, 2) simulated RNA-Seq data and 3) actual cancer cell-derived RNA-Seq data. Fcirc showed significant advantages over existing methods regarding both detection accuracy (i.e. precision, recall, F-measure) and computing performance (i.e. lower runtimes). Fcirc is a powerful and comprehensive python-based pipeline to identify linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computing times compared to previously published algorithms. Fcirc empowers the research community to study the biology of fusion RNAs in cancer more effectively.