title: Identifying fusion transcripts using next generation sequencing.
abstract: Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions have been used successfully for cancer diagnosis, prognosis, and therapeutic applications. In addition, many fusion transcripts are found in normal human cell lines and tissues, with some data supporting their role in normal physiology. Besides chromosomal rearrangement, intergenic splicing can generate them. Global identification of fusion transcripts becomes possible with the help of next generation sequencing technology like RNA-Seq. In the past decade, major advancements have been made for chimeric RNA discovery due to the development of advanced sequencing platform and software packages. However, current software tools behave differently in terms of specificity, sensitivity, time, and computational memory usage. Recent benchmarking studies showed that none of the tools are inclusive. The development of high performance (accurate and fast), and user-friendly fusion detection tool/pipeline is still an open quest. In this article, we review the existing software packages for fusion detection. We explain the methods of the tools, and discuss various factors that affect fusion detection. We summarize conclusions drawn from several comparative studies, and then discuss some of the pitfalls of these studies. We also describe the limitations of current tools, and suggest directions for future development. WIREs RNA 2016, 7:811-823. doi: 10.1002/wrna.1382 For further resources related to this article, please visit the WIREs website.
authors.affiliation: Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.
authors.forename: Shailesh
authors.initials: S
authors.lastname: Kumar
authors.affiliation: Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.
authors.forename: Sundus Khalid
authors.initials: SK
authors.lastname: Razzaq
authors.affiliation: Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.
authors.forename: Angie Duy
authors.initials: AD
authors.lastname: Vo
authors.affiliation: Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.
authors.forename: Mamta
authors.initials: M
authors.lastname: Gautam
authors.affiliation: Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA. hl9r@virginia.edu.|Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, USA. hl9r@virginia.edu.
authors.forename: Hui
authors.initials: H
authors.lastname: Li
mesh_terms: D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling; D050939:Gene Fusion; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D012984:Software