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A user guide to theBrassica60K Illumina Infinium™ SNP genotyping array

Overview of attention for article published in Theoretical and Applied Genetics, February 2017
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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 (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

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Title
A user guide to theBrassica60K Illumina Infinium™ SNP genotyping array
Published in
Theoretical and Applied Genetics, February 2017
DOI 10.1007/s00122-016-2849-1
Pubmed ID
Authors

Annaliese S. Mason, Erin E. Higgins, Rod J. Snowdon, Jacqueline Batley, Anna Stein, Christian Werner, Isobel A. P. Parkin

Abstract

The Brassica napus 60K Illumina Infinium™ SNP array has had huge international uptake in the rapeseed community due to the revolutionary speed of acquisition and ease of analysis of this high-throughput genotyping data, particularly when coupled with the newly available reference genome sequence. However, further utilization of this valuable resource can be optimized by better understanding the promises and pitfalls of SNP arrays. We outline how best to analyze Brassica SNP marker array data for diverse applications, including linkage and association mapping, genetic diversity and genomic introgression studies. We present data on which SNPs are locus-specific in winter, semi-winter and spring B. napus germplasm pools, rather than amplifying both an A-genome and a C-genome locus or multiple loci. Common issues that arise when analyzing array data will be discussed, particularly those unique to SNP markers and how to deal with these for practical applications in Brassica breeding applications.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 120 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 26%
Researcher 21 18%
Student > Master 13 11%
Other 7 6%
Student > Doctoral Student 5 4%
Other 11 9%
Unknown 32 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 66 55%
Biochemistry, Genetics and Molecular Biology 17 14%
Unspecified 1 <1%
Environmental Science 1 <1%
Computer Science 1 <1%
Other 1 <1%
Unknown 33 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 03 September 2020.
All research outputs
#3,454,502
of 23,794,258 outputs
Outputs from Theoretical and Applied Genetics
#376
of 3,565 outputs
Outputs of similar age
#63,787
of 311,720 outputs
Outputs of similar age from Theoretical and Applied Genetics
#13
of 49 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,565 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 89% 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 311,720 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 49 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 73% of its contemporaries.