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Single-cell TCRseq: paired recovery of entire T-cell alpha and beta chain transcripts in T-cell receptors from single-cell RNAseq

Overview of attention for article published in Genome Medicine, July 2016
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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 (89th percentile)

Mentioned by

twitter
28 tweeters
patent
1 patent

Citations

dimensions_citation
90 Dimensions

Readers on

mendeley
257 Mendeley
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Title
Single-cell TCRseq: paired recovery of entire T-cell alpha and beta chain transcripts in T-cell receptors from single-cell RNAseq
Published in
Genome Medicine, July 2016
DOI 10.1186/s13073-016-0335-7
Pubmed ID
Authors

David Redmond, Asaf Poran, Olivier Elemento

Abstract

Accurate characterization of the repertoire of the T-cell receptor (TCR) alpha and beta chains is critical to understanding adaptive immunity. Such characterization has many applications across such fields as vaccine development and response, clone-tracking in cancer, and immunotherapy. Here we present a new methodology called single-cell TCRseq (scTCRseq) for the identification and assembly of full-length rearranged V(D)J T-cell receptor sequences from paired-end single-cell RNA sequencing reads. The method allows accurate identification of the V(D)J rearrangements for each individual T-cell and has the novel ability to recover paired alpha and beta segments. Source code is available at https://github.com/ElementoLab/scTCRseq .

Twitter Demographics

The data shown below were collected from the profiles of 28 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 257 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Germany 1 <1%
South Africa 1 <1%
Unknown 254 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 60 23%
Researcher 58 23%
Student > Master 24 9%
Student > Bachelor 20 8%
Other 15 6%
Other 33 13%
Unknown 47 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 68 26%
Agricultural and Biological Sciences 53 21%
Immunology and Microbiology 35 14%
Medicine and Dentistry 20 8%
Engineering 8 3%
Other 22 9%
Unknown 51 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 29 September 2022.
All research outputs
#1,783,688
of 22,438,228 outputs
Outputs from Genome Medicine
#405
of 1,418 outputs
Outputs of similar age
#30,218
of 281,721 outputs
Outputs of similar age from Genome Medicine
#2
of 4 outputs
Altmetric has tracked 22,438,228 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 1,418 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.5. This one has gotten more attention than average, scoring higher than 71% 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 281,721 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 89% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.