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SQUID: transcriptomic structural variation detection from RNA-seq

Overview of attention for article published in Genome Biology (Online Edition), April 2018
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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 (86th percentile)

Mentioned by

twitter
30 tweeters

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
94 Mendeley
citeulike
2 CiteULike
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Title
SQUID: transcriptomic structural variation detection from RNA-seq
Published in
Genome Biology (Online Edition), April 2018
DOI 10.1186/s13059-018-1421-5
Pubmed ID
Authors

Cong Ma, Mingfu Shao, Carl Kingsford

Abstract

Transcripts are frequently modified by structural variations, which lead to fused transcripts of either multiple genes, known as a fusion gene, or a gene and a previously non-transcribed sequence. Detecting these modifications, called transcriptomic structural variations (TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to predict both fusion-gene and non-fusion-gene TSVs accurately from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model and doubles the precision on simulation data compared to other approaches. Using SQUID, we identify novel non-fusion-gene TSVs on TCGA samples.

Twitter Demographics

The data shown below were collected from the profiles of 30 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 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 94 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 27%
Student > Ph. D. Student 21 22%
Student > Master 10 11%
Other 6 6%
Student > Bachelor 6 6%
Other 16 17%
Unknown 10 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 34%
Biochemistry, Genetics and Molecular Biology 30 32%
Computer Science 8 9%
Medicine and Dentistry 3 3%
Engineering 2 2%
Other 5 5%
Unknown 14 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 22 February 2019.
All research outputs
#1,250,458
of 15,917,147 outputs
Outputs from Genome Biology (Online Edition)
#1,265
of 3,414 outputs
Outputs of similar age
#37,423
of 281,886 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 15,917,147 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 3,414 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.7. This one has gotten more attention than average, scoring higher than 62% 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 281,886 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 86% 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