Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate.
Here, Ewha Womans University researchers present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. They specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, the researchers have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. The researchers also provide several curative means to help users investigate the details of fusion candidates easily. They believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community.
Overview of FusionScan algorithm
Computational pipeline is shown with programs used in each step. The statistics for processing K562 RNA sequencing data illustrates the effect of each procedure on reducing the candidates of split reads and fusion gene pairs. ENCODE, Encyclopedia of DNA Elements.
Availability – FusionScan is freely available at http://fusionscan.ewha.ac.kr/