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Rapid identification of bovine MHCI haplotypes in genetically divergent cattle populations using next-generation sequencing

Overview of attention for article published in Immunogenetics, August 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 (77th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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Title
Rapid identification of bovine MHCI haplotypes in genetically divergent cattle populations using next-generation sequencing
Published in
Immunogenetics, August 2016
DOI 10.1007/s00251-016-0945-7
Pubmed ID
Authors

Deepali Vasoya, Andy Law, Paolo Motta, Mingyan Yu, Adrian Muwonge, Elizabeth Cook, Xiaoying Li, Karen Bryson, Amanda MacCallam, Tatjana Sitt, Philip Toye, Barend Bronsvoort, Mick Watson, W. Ivan Morrison, Timothy Connelley

Abstract

The major histocompatibility complex (MHC) region contains many genes that are key regulators of both innate and adaptive immunity including the polymorphic MHCI and MHCII genes. Consequently, the characterisation of the repertoire of MHC genes is critical to understanding the variation that determines the nature of immune responses. Our current knowledge of the bovine MHCI repertoire is limited with only the Holstein-Friesian breed having been studied in any depth. Traditional methods of MHCI genotyping are of low resolution and laborious and this has been a major impediment to a more comprehensive analysis of the MHCI repertoire of other cattle breeds. Next-generation sequencing (NGS) technologies have been used to enable high throughput and much higher resolution MHCI typing in a number of species. In this study we have developed a MiSeq platform approach and requisite bioinformatics pipeline to facilitate typing of bovine MHCI repertoires. The method was validated initially on a cohort of Holstein-Friesian animals and then demonstrated to enable characterisation of MHCI repertoires in African cattle breeds, for which there was limited or no available data. During the course of these studies we identified >140 novel classical MHCI genes and defined 62 novel MHCI haplotypes, dramatically expanding the known bovine MHCI repertoire.

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X Demographics

The data shown below were collected from the profiles of 13 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Argentina 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 38%
Student > Ph. D. Student 11 19%
Student > Bachelor 4 7%
Student > Master 4 7%
Student > Postgraduate 3 5%
Other 7 12%
Unknown 7 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 29%
Veterinary Science and Veterinary Medicine 12 21%
Biochemistry, Genetics and Molecular Biology 11 19%
Computer Science 3 5%
Engineering 3 5%
Other 3 5%
Unknown 9 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 13 May 2017.
All research outputs
#4,533,063
of 23,316,003 outputs
Outputs from Immunogenetics
#113
of 1,212 outputs
Outputs of similar age
#75,644
of 357,573 outputs
Outputs of similar age from Immunogenetics
#3
of 25 outputs
Altmetric has tracked 23,316,003 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 1,212 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 90% 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 357,573 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 77% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.