BioinformaticsBioinformaticsPeer-reviewed journal article
MetaFusion: A high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates.
Michael J. ApostolidesiD, Yue JiangiD, Mia HusićiD, Robert SiddawayiD, Cynthia E HawkinsiD, Andrei L. TurinskyiD, Michael BrudnoiD, Arun K RamaniiD
Abstract
MotivationCurrent fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. ResultsMetaFusion is a flexible meta-calling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format (CFF). Calls are annotated, merged using graph clustering, filtered, and ranked to provide a final output of high confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases, and is provided with a benchmarking toolkit to calibrate new callers. AvailabilityMetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion Contactarun.ramani@sickkids.ca Supplementary informationSupplementary data are available at Bioinformatics online.
- Citations
- 17
- FWCI
- 0.71
- Field percentile
- top 36%
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