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FusionQ: a novel approach for gene fusion detection and quantification from paired-end RNA-Seq

Chenglin Liu12, Jinwen Ma2, ChungChe (Jeff) Chang3 and Xiaobo Zhou1*

Author Affiliations

1 Department of Diagnostic Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA

2 Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing 100871, China

3 Department of Pathology Florida Hospital, University of Central Florida, Orlando, FI 32803, USA

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BMC Bioinformatics 2013, 14:193  doi:10.1186/1471-2105-14-193

Published: 15 June 2013

Abstract

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 webcite.

Keywords:
Fusion detection; chimerical transcripts quantification; EM algorithm