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Identifying fusion transcripts using next generation sequencing

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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
Fusion detection‐using split reads and spanning reads. (i) Paired‐end reads are mapped to reference sequences and discordantly mapped reads (spanning reads) are directly aligned to the target fusion genes. (ii) For paired‐end reads with one or both ends unaligned (potential split reads), the unmapped mate is cut into several pieces to be aligned to estimated fusion boundaries. Here, the two pieces of same read are connected by dashed line. (iii) Fusion candidate sequences are assembled. (iv) Assembled fusion sequences with highest probability to be selected as real fusion. Vertical dotted line represents the fusion junction.
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Two approaches for the initial steps of fusion detection. (a) ‘Mapping first’ approach of fusion detection. Paired‐end RNA‐Seq reads are first aligned to detect the fusion breakpoint through the alignment of ‘spanning reads’ and ‘split reads’ to the reference sequences. (b)‘Assembly first’ approach of fusion detection. In this approach, paired‐end RNA‐Seq reads are first assembled into the contigs (i.e., de novo transcriptome contigs) and then the contigs, having fusion junction, are aligned to reference sequences.
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Mechanism of fusion formation. (a) Fusions formed at DNA level (i.e., chromosomal rearrangements). At DNA level, gene fusion may originate through ‘balanced’ and ‘unbalanced’ chromosome rearrangements. ‘Balanced’ changes comprise translocations, insertion and inversion, whereas ‘unbalanced’ change that leads to fusion genes can be a deletion of an interstitial chromosomal segment. (b) RNA level fusions may occur through trans‐splicing or cis‐splicing between neighboring genes. Black blocks represent introns. In the ‘cis‐splicing’ mechanism, the red block represents intergenic region. The sizes of exon, intron, and intergenic regions are not drawn to scale.
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RNA in Disease and Development > RNA in Disease
RNA Processing > RNA Editing and Modification
RNA Evolution and Genomics > Computational Analyses of RNA

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