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JAFFA: High sensitivity transcriptome-focused fusion gene detection

Overview of attention for article published in Genome Medicine, May 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

blogs
2 blogs
twitter
51 tweeters
facebook
2 Facebook pages
q&a
1 Q&A thread

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
118 Mendeley
citeulike
4 CiteULike
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Title
JAFFA: High sensitivity transcriptome-focused fusion gene detection
Published in
Genome Medicine, May 2015
DOI 10.1186/s13073-015-0167-x
Pubmed ID
Authors

Nadia M Davidson, Ian J Majewski, Alicia Oshlack

Abstract

Genomic instability is a hallmark of cancer and, as such, structural alterations and fusion genes are common events in the cancer landscape. RNA sequencing (RNA-Seq) is a powerful method for profiling cancers, but current methods for identifying fusion genes are optimised for short reads. JAFFA (https://github.com/Oshlack/JAFFA/wiki) is a sensitive fusion detection method that outperforms other methods with reads of 100 bp or greater. JAFFA compares a cancer transcriptome to the reference transcriptome, rather than the genome, where the cancer transcriptome is inferred using long reads directly or by de novo assembling short reads.

Twitter Demographics

The data shown below were collected from the profiles of 51 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 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
Germany 3 3%
Brazil 1 <1%
Korea, Republic of 1 <1%
Norway 1 <1%
United Kingdom 1 <1%
New Zealand 1 <1%
Belgium 1 <1%
Indonesia 1 <1%
Other 1 <1%
Unknown 102 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 24%
Researcher 21 18%
Student > Master 20 17%
Student > Bachelor 13 11%
Other 7 6%
Other 18 15%
Unknown 11 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 43%
Biochemistry, Genetics and Molecular Biology 33 28%
Computer Science 12 10%
Medicine and Dentistry 5 4%
Unspecified 1 <1%
Other 2 2%
Unknown 14 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 40. 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 July 2019.
All research outputs
#465,379
of 14,167,291 outputs
Outputs from Genome Medicine
#100
of 998 outputs
Outputs of similar age
#9,814
of 230,021 outputs
Outputs of similar age from Genome Medicine
#1
of 1 outputs
Altmetric has tracked 14,167,291 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 998 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.7. This one has done well, scoring higher than 89% 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 230,021 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 95% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them