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pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens

Overview of attention for article published in Genome Medicine, January 2016
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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

news
1 news outlet
twitter
39 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
54 Dimensions

Readers on

mendeley
234 Mendeley
citeulike
3 CiteULike
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Title
pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens
Published in
Genome Medicine, January 2016
DOI 10.1186/s13073-016-0264-5
Pubmed ID
Authors

Hundal, Jasreet, Carreno, Beatriz M, Petti, Allegra A, Linette, Gerald P, Griffith, Obi L, Mardis, Elaine R, Griffith, Malachi, Jasreet Hundal, Beatriz M. Carreno, Allegra A. Petti, Gerald P. Linette, Obi L. Griffith, Elaine R. Mardis, Malachi Griffith

Abstract

Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at https://github.com/griffithlab/pVAC-Seq .

Twitter Demographics

The data shown below were collected from the profiles of 39 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 234 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
China 1 <1%
Taiwan 1 <1%
Japan 1 <1%
United Kingdom 1 <1%
Germany 1 <1%
Belgium 1 <1%
Korea, Republic of 1 <1%
Canada 1 <1%
Other 0 0%
Unknown 221 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 77 33%
Student > Ph. D. Student 49 21%
Student > Master 29 12%
Other 14 6%
Unspecified 14 6%
Other 51 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 67 29%
Biochemistry, Genetics and Molecular Biology 61 26%
Medicine and Dentistry 33 14%
Immunology and Microbiology 19 8%
Unspecified 17 7%
Other 37 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 02 October 2018.
All research outputs
#425,457
of 11,896,146 outputs
Outputs from Genome Medicine
#110
of 871 outputs
Outputs of similar age
#18,322
of 346,731 outputs
Outputs of similar age from Genome Medicine
#6
of 31 outputs
Altmetric has tracked 11,896,146 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 871 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.2. This one has done well, scoring higher than 87% 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 346,731 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 94% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.