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Statistical inference of a convergent antibody repertoire response to influenza vaccine

Overview of attention for article published in Genome Medicine, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)

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Title
Statistical inference of a convergent antibody repertoire response to influenza vaccine
Published in
Genome Medicine, June 2016
DOI 10.1186/s13073-016-0314-z
Pubmed ID
Authors

Nicolas B. Strauli, Ryan D. Hernandez

Abstract

Vaccines dramatically affect an individual's adaptive immune system and thus provide an excellent means to study human immunity. Upon vaccination, the B cells that express antibodies (Abs) that happen to bind the vaccine are stimulated to proliferate and undergo mutagenesis at their Ab locus. This process may alter the composition of B cell lineages within an individual, which are known collectively as the antibody repertoire (AbR). Antibodies are also highly expressed in whole blood, potentially enabling RNA sequencing (RNA-seq) technologies to query this diversity. Less is known about the diversity of AbR responses across individuals to a given vaccine and if individuals tend to yield a similar response to the same antigenic stimulus. Here we implement a bioinformatic pipeline that extracts the AbR information from a time-series RNA-seq dataset of five patients who were administered a seasonal trivalent influenza vaccine (TIV). We harness the detailed time-series nature of this dataset and use methods based in functional data analysis (FDA) to identify the Abs that respond to the vaccine. We then design and implement rigorous statistical tests in order to ask whether or not these patients exhibit a convergent AbR response to the same TIV. We find that high-resolution time-series data can be used to help identify the Abs that respond to an antigenic stimulus and that this response can exhibit a convergent nature across patients inoculated with the same vaccine. However, correlations in AbR diversity among individuals prior to inoculation can confound inference of a convergent signal unless it is taken into account. We developed a framework to identify the elements of an AbR that respond to an antigen. This information could be used to understand the diversity of different immune responses in different individuals, as well as to gauge the effectiveness of the immune response to a given stimulus within an individual. We also present a framework for testing a convergent hypothesis between AbRs; a hypothesis that is more difficult to test than previously appreciated. Our discovery of a convergent signal suggests that similar epitopes do select for antibodies with similar sequence characteristics.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 1%
United States 1 1%
Unknown 73 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 24%
Student > Ph. D. Student 13 17%
Student > Master 7 9%
Student > Bachelor 6 8%
Student > Postgraduate 4 5%
Other 15 20%
Unknown 12 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 20%
Biochemistry, Genetics and Molecular Biology 12 16%
Immunology and Microbiology 10 13%
Computer Science 5 7%
Engineering 5 7%
Other 12 16%
Unknown 16 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 12 June 2016.
All research outputs
#5,488,562
of 22,876,619 outputs
Outputs from Genome Medicine
#957
of 1,443 outputs
Outputs of similar age
#88,131
of 339,345 outputs
Outputs of similar age from Genome Medicine
#25
of 31 outputs
Altmetric has tracked 22,876,619 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,443 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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 339,345 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% 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 is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.