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

• Published in: BMC bioinformatics Vol. 14; no. 1; p. 193
• Main Authors: Liu, Chenglin, Ma, Jinwen, Chang, ChungChe (Jeff), Zhou, Xiaobo
• Format: Journal Article
• Language: English
• Published: London BioMed Central 15.06.2013; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Bioinformatics; Biomedical and Life Sciences; Breast Neoplasms - genetics; Cancer; Cell Line, Tumor; Colleges & universities; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Dynamic programming; Female; Gene Fusion; Genes; Genetic aspects; Genomes; Humans; Life Sciences; Microarrays; Physiological aspects; Programming languages; Proteins; RNA sequencing; Sequence analysis (methods); Sequence Analysis, RNA - methods; Software; Technological change; Transcriptome; United States; chimerical transcripts quantification; EM algorithm; Fusion detection
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-14-193

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 .