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Given the unprecedented sensitivity of gene fusion detection, and the repeated identification of fusion transcripts in normal cells, it is increasingly important to separate driver fusions from passenger mutations. Although many fusion detection tools encode their own filters in order to cut down on false positive calls (Beccuti et al., 2013), the criteria are most often based on read mapping quality and the presence of certain sequence features. Biological approaches that rank fusion candidates by some notion of functional importance are complementary and can offer a significant improvement in removing false positive calls. Source text: Latysheva and Babu, 2016.