↓ Skip to main content

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

Overview of attention for article published in PLoS Computational Biology, May 2011
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

blogs
1 blog
twitter
6 tweeters
f1000
1 research highlight platform
q&a
1 Q&A thread

Readers on

mendeley
391 Mendeley
citeulike
8 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
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
Authors

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

Abstract

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

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 391 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 3%
United Kingdom 5 1%
France 4 1%
Canada 4 1%
Italy 3 <1%
Korea, Republic of 3 <1%
Norway 2 <1%
Netherlands 2 <1%
Japan 2 <1%
Other 13 3%
Unknown 341 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 130 33%
Student > Ph. D. Student 109 28%
Student > Master 48 12%
Professor > Associate Professor 18 5%
Student > Bachelor 17 4%
Other 69 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 216 55%
Biochemistry, Genetics and Molecular Biology 64 16%
Computer Science 40 10%
Medicine and Dentistry 33 8%
Unspecified 11 3%
Other 27 7%

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
#688,831
of 8,931,063 outputs
Outputs from PLoS Computational Biology
#1,011
of 4,303 outputs
Outputs of similar age
#669,499
of 8,296,386 outputs
Outputs of similar age from PLoS Computational Biology
#1,007
of 4,260 outputs
Altmetric has tracked 8,931,063 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,303 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.8. 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 8,296,386 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 91% of its contemporaries.
We're also able to compare this research output to 4,260 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.