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Identifying fusion transcripts using next generation sequencing
Author(s) -
Kumar Shailesh,
Razzaq Sundus Khalid,
Vo Angie Duy,
Gautam Mamta,
Li Hui
Publication year - 2016
Publication title -
wiley interdisciplinary reviews: rna
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.225
H-Index - 71
eISSN - 1757-7012
pISSN - 1757-7004
DOI - 10.1002/wrna.1382
Subject(s) - computational biology , rna , rna splicing , fusion gene , biology , pipeline (software) , rna editing , identification (biology) , computer science , gene , bioinformatics , genetics , botany , programming language
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 This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > RNA Editing and Modification RNA in Disease and Development > RNA in Disease