Abbreviated Journal Title
Fusion detection; chimerical transcripts quantification; EM algorithm; BREAST-CANCER; TRANSCRIPTS; GENOME; DISCOVERY; DNA; Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology
Background: Gene fusions, which result from abnormal chromosome rearrangements, are a pathogenic factor in cancer development. The emerging RNA-Seq technology enables us to detect gene fusions and profile their features. Results: In this paper, we proposed a novel fusion detection tool, FusionQ, based on paired-end RNA-Seq data. This tool can detect gene fusions, construct the structures of chimerical transcripts, and estimate their abundances. To confirm the read alignment on both sides of a fusion point, we employed a new approach, "residual sequence extension", which extended the short segments of the reads by aggregating their overlapping reads. We also proposed a list of filters to control the false-positive rate. In addition, we estimated fusion abundance using the Expectation-Maximization algorithm with sparse optimization, and further adopted it to improve the detection accuracy of the fusion transcripts. Simulation was performed by FusionQ and another two stated-of-art fusion detection tools. FusionQ exceeded the other two in both sensitivity and specificity, especially in low coverage fusion detection. Using paired-end RNA-Seq data from breast cancer cell lines, FusionQ detected both the previously reported and new fusions. FusionQ reported the structures of these fusions and provided their expressions. Some highly expressed fusion genes detected by FusionQ are important biomarkers in breast cancer. The performances of FusionQ on cancel line data still showed better specificity and sensitivity in the comparison with another two tools. Conclusions: FusionQ is a novel tool for fusion detection and quantification based on RNA-Seq data. It has both good specificity and sensitivity performance. FusionQ is free and available at http://www.wakehealth.edu/CTSB/Software/Software.htm.
Liu, Chenglin; Ma, Jinwen; Chang, Chung-Che "Jeff"; and Zhou, Xiaobo, "FusionQ: a novel approach for gene fusion detection and quantification from paired-end RNA-Seq" (2013). Faculty Bibliography 2010s. 4319.