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FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data

Overview of attention for article published in Genome Biology, October 2010
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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 (83rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

patent
1 patent
wikipedia
2 Wikipedia pages
q&a
1 Q&A thread

Citations

dimensions_citation
137 Dimensions

Readers on

mendeley
191 Mendeley
citeulike
12 CiteULike
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Title
FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data
Published in
Genome Biology, October 2010
DOI 10.1186/gb-2010-11-10-r104
Pubmed ID
Authors

Andrea Sboner, Lukas Habegger, Dorothee Pflueger, Stephane Terry, David Z Chen, Joel S Rozowsky, Ashutosh K Tewari, Naoki Kitabayashi, Benjamin J Moss, Mark S Chee, Francesca Demichelis, Mark A Rubin, Mark B Gerstein

Abstract

We have developed FusionSeq to identify fusion transcripts from paired-end RNA-sequencing. FusionSeq includes filters to remove spurious candidate fusions with artifacts, such as misalignment or random pairing of transcript fragments, and it ranks candidates according to several statistics. It also has a module to identify exact sequences at breakpoint junctions. FusionSeq detected known and novel fusions in a specially sequenced calibration data set, including eight cancers with and without known rearrangements.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 6 3%
United States 4 2%
France 2 1%
Norway 2 1%
Korea, Republic of 2 1%
Switzerland 1 <1%
Netherlands 1 <1%
Italy 1 <1%
Austria 1 <1%
Other 6 3%
Unknown 165 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 65 34%
Student > Ph. D. Student 46 24%
Professor > Associate Professor 17 9%
Student > Master 17 9%
Other 8 4%
Other 22 12%
Unknown 16 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 100 52%
Biochemistry, Genetics and Molecular Biology 38 20%
Computer Science 15 8%
Medicine and Dentistry 13 7%
Mathematics 2 1%
Other 4 2%
Unknown 19 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 30 August 2023.
All research outputs
#4,191,334
of 25,371,288 outputs
Outputs from Genome Biology
#2,623
of 4,467 outputs
Outputs of similar age
#18,131
of 108,564 outputs
Outputs of similar age from Genome Biology
#16
of 39 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
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 108,564 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.