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Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data

Overview of attention for article published in Genome Biology (Online Edition), January 2015
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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 (93rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

blogs
1 blog
twitter
21 tweeters
facebook
1 Facebook page
q&a
1 Q&A thread

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
139 Mendeley
citeulike
4 CiteULike
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Title
Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data
Published in
Genome Biology (Online Edition), January 2015
DOI 10.1186/s13059-014-0558-0
Pubmed ID
Authors

Lynnette Fernandez-Cuesta, Ruping Sun, Roopika Menon, Julie George, Susanne Lorenz, Leonardo A Meza-Zepeda, Martin Peifer, Dennis Plenker, Johannes M Heuckmann, Frauke Leenders, Thomas Zander, Ilona Dahmen, Mirjam Koker, Jakob Schöttle, Roland T Ullrich, Janine Altmüller, Christian Becker, Peter Nürnberg, Henrik Seidel, Diana Böhm, Friederike Göke, Sascha Ansén, Prudence A Russell, Gavin M Wright, Zoe Wainer, Benjamin Solomon, Iver Petersen, Joachim H Clement, Jörg Sänger, Odd-Terje Brustugun, Åslaug Helland, Steinar Solberg, Marius Lund-Iversen, Reinhard Buettner, Jürgen Wolf, Elisabeth Brambilla, Martin Vingron, Sven Perner, Stefan A Haas, Roman K Thomas

Abstract

Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline) (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 4%
Germany 3 2%
Netherlands 1 <1%
Norway 1 <1%
Switzerland 1 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Belgium 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 122 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 43 31%
Student > Ph. D. Student 38 27%
Student > Doctoral Student 11 8%
Student > Master 11 8%
Student > Bachelor 7 5%
Other 17 12%
Unknown 12 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 37%
Biochemistry, Genetics and Molecular Biology 35 25%
Medicine and Dentistry 20 14%
Computer Science 9 6%
Engineering 2 1%
Other 8 6%
Unknown 14 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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
#1,069,275
of 16,638,522 outputs
Outputs from Genome Biology (Online Edition)
#1,064
of 3,508 outputs
Outputs of similar age
#19,969
of 292,660 outputs
Outputs of similar age from Genome Biology (Online Edition)
#5
of 11 outputs
Altmetric has tracked 16,638,522 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 3,508 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.2. This one has gotten more attention than average, scoring higher than 69% 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 292,660 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 93% of its contemporaries.
We're also able to compare this research output to 11 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 63% of its contemporaries.