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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

news
2 news outlets
twitter
33 X users
patent
2 patents
googleplus
1 Google+ user

Citations

dimensions_citation
340 Dimensions

Readers on

mendeley
465 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

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 .

X Demographics

X Demographics

The data shown below were collected from the profiles of 33 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 465 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 1%
Korea, Republic of 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Germany 1 <1%
Belgium 1 <1%
Taiwan 1 <1%
Japan 1 <1%
China 1 <1%
Other 0 0%
Unknown 452 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 113 24%
Student > Ph. D. Student 90 19%
Student > Master 45 10%
Student > Bachelor 30 6%
Other 26 6%
Other 70 15%
Unknown 91 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 117 25%
Agricultural and Biological Sciences 86 18%
Immunology and Microbiology 49 11%
Medicine and Dentistry 46 10%
Computer Science 29 6%
Other 37 8%
Unknown 101 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 41. 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 18 September 2021.
All research outputs
#988,901
of 25,284,710 outputs
Outputs from Genome Medicine
#193
of 1,566 outputs
Outputs of similar age
#17,736
of 408,644 outputs
Outputs of similar age from Genome Medicine
#7
of 42 outputs
Altmetric has tracked 25,284,710 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 1,566 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.0. 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 408,644 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 42 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.