Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.17.302307v1?rss=1 Authors: Apostolides, M., Jiang, Y., Husic, M., Siddaway, R., Hawkins, C., Turinsky, A., Brudno, M., Ramani, A. Abstract: Motivation: Gene fusions are often associated with cancer, yet current fusion detection tools vary in their calling approaches, making selecting the right tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. Results: MetaFusion is a flexible meta-calling tool that amalgamates the outputs from any number of fusion callers. Results from individual callers are converted into Common Fusion Format, a new file type that standardizes outputs from callers. Calls are then annotated, merged using graph clustering, filtered and ranked to provide a final output of high confidence candidates. MetaFusion consistently outperformed individual callers with respect to recall and precision on real and simulated datasets, achieving up to 100% precision. Thus, an ensemble calling approach is imperative for high confidence results. MetaFusion also labels fusions found in databases using the FusionAnnotator package, and is provided with a benchmarking toolkit to calibrate new callers. Availability: MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion Copy rights belong to original authors. Visit the link for more info