Poster abstracts
Poster number 71 submitted by Jackson Killian
A tool for fusion detector post-analysis coverage visualization of chimeric RNA-seq data
Jackson A Killian (Department of Physics, The Ohio State University), Taha Topiwala (Department of Physics, The Ohio State University), Alex Pelletier (Department of Physics, The Ohio State University), David Frankhouser (Biomedical Sciences Graduate Program, The Ohio State University), Pearlly Yan (The Ohio State University Comprehensive Cancer Center and Division of Hematology, Department of Internal Medicine), Ralf Bundschuh (Department of Physics, Department of Chemistry and Biochemistry, Division of Hematology, Center for RNA Biology, The Ohio State University)
Abstract:
Gene fusions often occur in cancer cells and in some cases can even be the cause of the onset of the disease. Correct identification of oncogenic gene fusions thus has implications for targeted treatment in future therapy courses. Recognition of this potential along with the advent of RNA-seq has led to the development of a myriad of sequencing-based fusion detection tools designed to detect novel fusion transcripts in RNA data. However, given the same input, many of these detectors will find different fusion points or claim different sets of supporting data. Furthermore, the rate at which these tools falsely detect fusion events in data varies greatly. This discrepancy between tools underscores the fact that computational methods still cannot perfectly evaluate evidence; especially when provided with small amounts of supporting data as is typical in fusion detection. If data is provided in an easily digestible form, humans are more proficient at drawing conclusions. We thus have developed a visualization tool, FuSpot, that puts the power to decide false positives in the researcher’s hands, saving time and resources by cutting down on costly and labor intensive PCR validation. FuSpot is given the coordinates of a candidate fusion junction and a set of reads aligning in the vicinity of the candidate fusion junction. FuSpot then realigns the reads simultaneously to genomic sequences as well as the sequences of the nearest exons for both candidate fusion gene partners and visualizes them relative to the reference sequences on either side of the fusion point so that researchers can quickly visually confirm if their data does indeed support the presence of the fusion. We apply our visualization tool to a publicly available dataset and provide examples of true as well as false positives reported by commonly used fusion detection tools.
Keywords: Fusion, Visualization, RNA-Seq