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FusionFinder: A Software Tool to Identify Expressed Gene Fusion Candidates from RNA-Seq Data

Overview of attention for article published in PLOS ONE, June 2012
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Title
FusionFinder: A Software Tool to Identify Expressed Gene Fusion Candidates from RNA-Seq Data
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0039987
Pubmed ID
Authors

Richard W. Francis, Katherine Thompson-Wicking, Kim W. Carter, Denise Anderson, Ursula R. Kees, Alex H. Beesley

Abstract

The hallmarks of many haematological malignancies and solid tumours are chromosomal translocations, which may lead to gene fusions. Recently, next-generation sequencing techniques at the transcriptome level (RNA-Seq) have been used to verify known and discover novel transcribed gene fusions. We present FusionFinder, a Perl-based software designed to automate the discovery of candidate gene fusion partners from single-end (SE) or paired-end (PE) RNA-Seq read data. FusionFinder was applied to data from a previously published analysis of the K562 chronic myeloid leukaemia (CML) cell line. Using FusionFinder we successfully replicated the findings of this study and detected additional previously unreported fusion genes in their dataset, which were confirmed experimentally. These included two isoforms of a fusion involving the genes BRK1 and VHL, whose co-deletion has previously been associated with the prevalence and severity of renal-cell carcinoma. FusionFinder is made freely available for non-commercial use and can be downloaded from the project website (http://bioinformatics.childhealthresearch.org.au/software/fusionfinder/).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
United Kingdom 2 2%
Norway 1 <1%
Korea, Republic of 1 <1%
Sweden 1 <1%
Germany 1 <1%
Canada 1 <1%
France 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 97 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 32%
Student > Ph. D. Student 31 28%
Student > Master 16 14%
Professor > Associate Professor 5 5%
Other 4 4%
Other 10 9%
Unknown 10 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 44%
Biochemistry, Genetics and Molecular Biology 24 22%
Computer Science 9 8%
Medicine and Dentistry 9 8%
Engineering 5 5%
Other 4 4%
Unknown 11 10%