↓ Skip to main content

Influenza Virus

Overview of attention for book
Cover of 'Influenza Virus'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Understanding Influenza
  3. Altmetric Badge
    Chapter 2 Clinical Diagnosis of Influenza
  4. Altmetric Badge
    Chapter 3 Influenza A Virus Genetic Tools: From Clinical Sample to Molecular Clone
  5. Altmetric Badge
    Chapter 4 Propagation and Titration of Influenza Viruses
  6. Altmetric Badge
    Chapter 5 Purification and Proteomics of Influenza Virions
  7. Altmetric Badge
    Chapter 6 Haploid Screening for the Identification of Host Factors in Virus Infection
  8. Altmetric Badge
    Chapter 7 Phenotypic Lentivirus Screens to Identify Antiviral Single Domain Antibodies
  9. Altmetric Badge
    Chapter 8 Deciphering Virus Entry with Fluorescently Labeled Viral Particles
  10. Altmetric Badge
    Chapter 9 Quantitative RT-PCR Analysis of Influenza Virus Endocytic Escape
  11. Altmetric Badge
    Chapter 10 Single-Molecule Sensitivity RNA FISH Analysis of Influenza Virus Genome Trafficking
  12. Altmetric Badge
    Chapter 11 3D Electron Microscopy (EM) and Correlative Light Electron Microscopy (CLEM) Methods to Study Virus-Host Interactions
  13. Altmetric Badge
    Chapter 12 Correlative Light and Electron Microscopy of Influenza Virus Entry and Budding
  14. Altmetric Badge
    Chapter 13 Influenza Virus-Liposome Fusion Studies Using Fluorescence Dequenching and Cryo-electron Tomography
  15. Altmetric Badge
    Chapter 14 Metal-Tagging Transmission Electron Microscopy and Immunogold Labeling on Tokuyasu Cryosections to Image Influenza A Virus Ribonucleoprotein Transport and Packaging
  16. Altmetric Badge
    Chapter 15 Live Imaging of Influenza Viral Ribonucleoproteins Using Light-Sheet Microscopy
  17. Altmetric Badge
    Chapter 16 Purification of Unanchored Polyubiquitin Chains from Influenza Virions
  18. Altmetric Badge
    Chapter 17 Assays to Measure the Activity of Influenza Virus Polymerase
  19. Altmetric Badge
    Chapter 18 In Vitro Models to Study Influenza Virus and Staphylococcus aureus Super-Infection on a Molecular Level
  20. Altmetric Badge
    Chapter 19 Infection of Cultured Mammalian Cells with Aerosolized Influenza Virus
  21. Altmetric Badge
    Chapter 20 Animal Models in Influenza Research
  22. Altmetric Badge
    Chapter 21 Measuring Influenza Virus Infection Using Bioluminescent Reporter Viruses for In Vivo Imaging and In Vitro Replication Assays
  23. Altmetric Badge
    Chapter 22 Selection of Antigenically Advanced Variants of Influenza Viruses
  24. Altmetric Badge
    Chapter 23 Assessment of Influenza Virus Hemagglutinin Stalk-Specific Antibody Responses
  25. Altmetric Badge
    Chapter 24 Analyses of Cellular Immune Responses in Ferrets Following Influenza Virus Infection
  26. Altmetric Badge
    Chapter 25 Parameter Estimation in Mathematical Models of Viral Infections Using R
  27. Altmetric Badge
    Chapter 26 Software for Characterizing the Antigenic and Genetic Evolution of Human Influenza Viruses
  28. Altmetric Badge
    Chapter 27 Clinical Trials of Influenza Vaccines: Special Challenges
  29. Altmetric Badge
    Chapter 28 The Silver Lining in Gain-of-Function Experiments with Pathogens of Pandemic Potential
  30. Altmetric Badge
    Chapter 29 Why Do Exceptionally Dangerous Gain-of-Function Experiments in Influenza?
  31. Altmetric Badge
    Chapter 30 How Computational Models Enable Mechanistic Insights into Virus Infection
Attention for Chapter 26: Software for Characterizing the Antigenic and Genetic Evolution of Human Influenza Viruses
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
10 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Software for Characterizing the Antigenic and Genetic Evolution of Human Influenza Viruses
Chapter number 26
Book title
Influenza Virus
Published in
Methods in molecular biology, August 2018
DOI 10.1007/978-1-4939-8678-1_26
Pubmed ID
Book ISBNs
978-1-4939-8677-4, 978-1-4939-8678-1
Authors

Susanne Reimering, Alice C. McHardy, Reimering, Susanne, McHardy, Alice C.

Abstract

Influenza viruses are rapidly evolving pathogens causing annual epidemics and occasional pandemics. The accumulation of amino acid substitutions allows the virus to adapt to changing environments like novel host species or to escape the acquired immunity of the host population. Especially substitutions in the epitope regions of the surface protein HA lead to antigenic change, facilitating the evasion of the host's immune response by the virus and making frequent updates of the vaccine composition necessary. Through the global monitoring of circulating influenza viruses, large amounts of sequence data are generated. Computational biology offers a quick and easy way to analyze these to characterize the genetic and antigenic evolution of influenza viruses. Using sequence data together with antigenic information provided by hemagglutination inhibition (HI) assays and structural information, bioinformatics methods can elucidate evolutionary relationships between isolates, infer amino acid sites or regions of the protein under positive selection, and identify amino acid changes relevant for the antigenic evolution. We here describe a selection of programs used to generate hypotheses about functionally or antigenically important amino acid changes, protein regions, or individual sites that can subsequently be tested in wet-lab experiments or have value for predicting the future evolution of seasonal influenza A viruses.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 30%
Student > Master 2 20%
Student > Doctoral Student 1 10%
Other 1 10%
Researcher 1 10%
Other 0 0%
Unknown 2 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 10%
Mathematics 1 10%
Agricultural and Biological Sciences 1 10%
Computer Science 1 10%
Immunology and Microbiology 1 10%
Other 2 20%
Unknown 3 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 29 August 2018.
All research outputs
#20,726,217
of 23,325,355 outputs
Outputs from Methods in molecular biology
#10,111
of 13,337 outputs
Outputs of similar age
#292,566
of 335,449 outputs
Outputs of similar age from Methods in molecular biology
#192
of 250 outputs
Altmetric has tracked 23,325,355 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,337 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% 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 335,449 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 250 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.