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deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data

Overview of attention for article published in PLoS Computational Biology, May 2011
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

1 blog
6 tweeters
1 research highlight platform
1 Q&A thread


280 Dimensions

Readers on

410 Mendeley
8 CiteULike
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deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data
Published in
PLoS Computational Biology, May 2011
DOI 10.1371/journal.pcbi.1001138
Pubmed ID

Andrew McPherson, Fereydoun Hormozdiari, Abdalnasser Zayed, Ryan Giuliany, Gavin Ha, Mark G. F. Sun, Malachi Griffith, Alireza Heravi Moussavi, Janine Senz, Nataliya Melnyk, Marina Pacheco, Marco A. Marra, Martin Hirst, Torsten O. Nielsen, S. Cenk Sahinalp, David Huntsman, Sohrab P. Shah, Scott Markel


Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 410 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 3%
United Kingdom 5 1%
Canada 4 <1%
France 4 <1%
Italy 3 <1%
Korea, Republic of 3 <1%
Norway 2 <1%
Germany 2 <1%
Japan 2 <1%
Other 12 3%
Unknown 362 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 130 32%
Student > Ph. D. Student 113 28%
Student > Master 54 13%
Student > Doctoral Student 20 5%
Professor > Associate Professor 19 5%
Other 74 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 216 53%
Biochemistry, Genetics and Molecular Biology 68 17%
Computer Science 41 10%
Medicine and Dentistry 35 9%
Unspecified 14 3%
Other 36 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 May 2017.
All research outputs
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Outputs from PLoS Computational Biology
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Outputs of similar age
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Outputs of similar age from PLoS Computational Biology
of 4,561 outputs
Altmetric has tracked 11,348,182 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,603 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.8. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 10,682,059 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 4,561 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.