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Autologous Antibody Capture to Enrich Immunogenic Viruses for Viral Discovery

Overview of attention for article published in PLoS ONE, November 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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5 tweeters
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2 Facebook pages

Readers on

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37 Mendeley
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2 CiteULike
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Title
Autologous Antibody Capture to Enrich Immunogenic Viruses for Viral Discovery
Published in
PLoS ONE, November 2013
DOI 10.1371/journal.pone.0078454
Pubmed ID
Authors

Bas B. Oude Munnink, Seyed Mohammad Jazaeri Farsani, Martin Deijs, Jiri Jonkers, Joost T. P. Verhoeven, Margareta Ieven, Herman Goossens, Menno D. de Jong, Ben Berkhout, Katherine Loens, Paul Kellam, Margreet Bakker, Marta Canuti, Matthew Cotten, Lia van der Hoek

Abstract

Discovery of new viruses has been boosted by novel deep sequencing technologies. Currently, many viruses can be identified by sequencing without knowledge of the pathogenicity of the virus. However, attributing the presence of a virus in patient material to a disease in the patient can be a challenge. One approach to meet this challenge is identification of viral sequences based on enrichment by autologous patient antibody capture. This method facilitates identification of viruses that have provoked an immune response within the patient and may increase the sensitivity of the current virus discovery techniques. To demonstrate the utility of this method, virus discovery deep sequencing (VIDISCA-454) was performed on clinical samples from 19 patients: 13 with a known respiratory viral infection and 6 with a known gastrointestinal viral infection. Patient sera was collected from one to several months after the acute infection phase. Input and antibody capture material was sequenced and enrichment was assessed. In 18 of the 19 patients, viral reads from immunogenic viruses were enriched by antibody capture (ranging between 1.5x to 343x in respiratory material, and 1.4x to 53x in stool). Enriched reads were also determined in an identity independent manner by using a novel algorithm Xcompare. In 16 of the 19 patients, 21% to 100% of the enriched reads were derived from infecting viruses. In conclusion, the technique provides a novel approach to specifically identify immunogenic viral sequences among the bulk of sequences which are usually encountered during virus discovery metagenomics.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 5%
Spain 1 3%
South Africa 1 3%
Unknown 33 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 24%
Researcher 8 22%
Student > Master 6 16%
Student > Bachelor 3 8%
Student > Postgraduate 3 8%
Other 8 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 57%
Biochemistry, Genetics and Molecular Biology 5 14%
Unspecified 3 8%
Medicine and Dentistry 3 8%
Computer Science 2 5%
Other 3 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 17 December 2013.
All research outputs
#1,331,832
of 6,920,696 outputs
Outputs from PLoS ONE
#26,814
of 102,330 outputs
Outputs of similar age
#28,993
of 143,345 outputs
Outputs of similar age from PLoS ONE
#1,139
of 4,040 outputs
Altmetric has tracked 6,920,696 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 102,330 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 10.0. This one has gotten more attention than average, scoring higher than 73% 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 143,345 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 79% of its contemporaries.
We're also able to compare this research output to 4,040 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.