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STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq

By Brian J. Haas, Alex Dobin, Nicolas Stransky, Bo Li, Xiao Yang, Timothy Tickle, Asma Bankapur, Carrie Ganote, Thomas G. Doak, Nathalie Pochet, Jing Sun, Catherine J. Wu, Thomas R. Gingeras, Aviv Regev

Posted 24 Mar 2017
bioRxiv DOI: 10.1101/120295

Motivation: Fusion genes created by genomic rearrangements can be potent drivers of tumorigenesis. However, accurate identification of functionally fusion genes from genomic sequencing requires whole genome sequencing, since exonic sequencing alone is often insufficient. Transcriptome sequencing provides a direct, highly effective alternative for capturing molecular evidence of expressed fusions in the precision medicine pipeline, but current methods tend to be inefficient or insufficiently accurate, lacking in sensitivity or predicting large numbers of false positives. Here, we describe STAR-Fusion, a method that is both fast and accurate in identifying fusion transcripts from RNA-Seq data. Results: We benchmarked STAR-Fusion's fusion detection accuracy using both simulated and genuine Illumina paired-end RNA-Seq data, and show that it has superior performance compared to popular alternative fusion detection methods. Availability and implementation: STAR-Fusion is implemented in Perl, freely available as open source software at http://star-fusion.github.io, and supported on Linux.

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