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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

Readers on

mendeley
166 Mendeley
citeulike
2 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 166 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
China 2 1%
Germany 1 <1%
United Kingdom 1 <1%
Taiwan 1 <1%
Japan 1 <1%
Belgium 1 <1%
Korea, Republic of 1 <1%
Canada 1 <1%
Other 0 0%
Unknown 152 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 63 38%
Student > Ph. D. Student 37 22%
Student > Master 21 13%
Other 11 7%
Student > Postgraduate 9 5%
Other 25 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 36%
Biochemistry, Genetics and Molecular Biology 47 28%
Medicine and Dentistry 24 14%
Computer Science 12 7%
Immunology and Microbiology 9 5%
Other 15 9%

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 28 April 2017.
All research outputs
#310,778
of 8,566,293 outputs
Outputs from Genome Medicine
#85
of 749 outputs
Outputs of similar age
#17,430
of 338,075 outputs
Outputs of similar age from Genome Medicine
#6
of 31 outputs
Altmetric has tracked 8,566,293 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 749 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.7. This one has done well, scoring higher than 88% 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 338,075 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.