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Reanalysis of RNA-Sequencing Data Reveals Several Additional Fusion Genes with Multiple Isoforms

Overview of attention for article published in PLOS ONE, October 2012
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Title
Reanalysis of RNA-Sequencing Data Reveals Several Additional Fusion Genes with Multiple Isoforms
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0048745
Pubmed ID
Authors

Sara Kangaspeska, Susanne Hultsch, Henrik Edgren, Daniel Nicorici, Astrid Murumägi, Olli Kallioniemi

Abstract

RNA-sequencing and tailored bioinformatic methodologies have paved the way for identification of expressed fusion genes from the chaotic genomes of solid tumors. We have recently successfully exploited RNA-sequencing for the discovery of 24 novel fusion genes in breast cancer. Here, we demonstrate the importance of continuous optimization of the bioinformatic methodology for this purpose, and report the discovery and experimental validation of 13 additional fusion genes from the same samples. Integration of copy number profiling with the RNA-sequencing results revealed that the majority of the gene fusions were promoter-donating events that occurred at copy number transition points or involved high-level DNA-amplifications. Sequencing of genomic fusion break points confirmed that DNA-level rearrangements underlie selected fusion transcripts. Furthermore, a significant portion (>60%) of the fusion genes were alternatively spliced. This illustrates the importance of reanalyzing sequencing data as gene definitions change and bioinformatic methods improve, and highlights the previously unforeseen isoform diversity among fusion transcripts.

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Geographical breakdown

Country Count As %
Germany 1 1%
France 1 1%
Norway 1 1%
Italy 1 1%
United Kingdom 1 1%
Singapore 1 1%
Unknown 69 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 29%
Researcher 17 23%
Student > Master 11 15%
Student > Bachelor 8 11%
Professor > Associate Professor 4 5%
Other 8 11%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 44%
Biochemistry, Genetics and Molecular Biology 17 23%
Computer Science 7 9%
Medicine and Dentistry 7 9%
Engineering 4 5%
Other 1 1%
Unknown 6 8%