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BreakTrans: uncovering the genomic architecture of gene fusions

Overview of attention for article published in Genome Biology, August 2013
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3 X users

Citations

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27 Dimensions

Readers on

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64 Mendeley
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2 CiteULike
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Title
BreakTrans: uncovering the genomic architecture of gene fusions
Published in
Genome Biology, August 2013
DOI 10.1186/gb-2013-14-8-r87
Pubmed ID
Authors

Ken Chen, Nicholas E Navin, Yong Wang, Heather K Schmidt, John W Wallis, Beifang Niu, Xian Fan, Hao Zhao, Michael D McLellan, Katherine A Hoadley, Elaine R Mardis, Timothy J Ley, Charles M Perou, Richard K Wilson, Li Ding

Abstract

Producing gene fusions through genomic structural rearrangements is a major mechanism for tumor evolution. Therefore, accurately detecting gene fusions and the originating rearrangements is of great importance for personalized cancer diagnosis and targeted therapy. We present a tool, BreakTrans, that systematically maps predicted gene fusions to structural rearrangements. Thus, BreakTrans not only validates both types of predictions, but also provides mechanistic interpretations. BreakTrans effectively validates known fusions and discovers novel events in a breast cancer cell line. Applying BreakTrans to 43 breast cancer samples in The Cancer Genome Atlas identifies 90 genomically validated gene fusions. BreakTrans is available at http://bioinformatics.mdanderson.org/main/BreakTrans.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 6%
Brazil 1 2%
Unknown 59 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 25%
Researcher 16 25%
Professor 6 9%
Other 4 6%
Student > Bachelor 3 5%
Other 13 20%
Unknown 6 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 42%
Biochemistry, Genetics and Molecular Biology 15 23%
Computer Science 5 8%
Medicine and Dentistry 3 5%
Engineering 2 3%
Other 5 8%
Unknown 7 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 25 September 2018.
All research outputs
#15,169,543
of 25,373,627 outputs
Outputs from Genome Biology
#3,931
of 4,467 outputs
Outputs of similar age
#114,618
of 210,767 outputs
Outputs of similar age from Genome Biology
#40
of 50 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
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 11th percentile – i.e., 11% 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 210,767 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.