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Routine performance and errors of 454 HLA exon sequencing in diagnostics

Overview of attention for article published in BMC Bioinformatics, June 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 (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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Title
Routine performance and errors of 454 HLA exon sequencing in diagnostics
Published in
BMC Bioinformatics, June 2013
DOI 10.1186/1471-2105-14-176
Pubmed ID
Authors

Norbert Niklas, Johannes Pröll, Martin Danzer, Stephanie Stabentheiner, Katja Hofer, Christian Gabriel

Abstract

Next-generation sequencing (NGS) has changed genomics significantly. More and more applications strive for sequencing with different platforms. Now, in 2012, after a decade of development and evolution, NGS has been accepted for a variety of research fields. Determination of sequencing errors is essential in order to follow next-generation sequencing beyond research use only. This study describes the overall 454 system performance of using multiple GS Junior runs with an in-house established and validated diagnostic assay for human leukocyte antigen (HLA) exon sequencing. Based on this data, we extracted, evaluated and characterized errors and variants of 60 HLA loci per run with respect to their adjacencies.

X Demographics

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The data shown below were collected from the profiles of 10 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Sweden 1 2%
United Arab Emirates 1 2%
Spain 1 2%
Russia 1 2%
Unknown 35 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 27%
Student > Ph. D. Student 7 17%
Student > Bachelor 6 15%
Student > Doctoral Student 4 10%
Other 4 10%
Other 8 20%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 46%
Biochemistry, Genetics and Molecular Biology 7 17%
Computer Science 3 7%
Medicine and Dentistry 3 7%
Engineering 3 7%
Other 2 5%
Unknown 4 10%
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 07 November 2013.
All research outputs
#5,655,478
of 22,711,645 outputs
Outputs from BMC Bioinformatics
#2,110
of 7,259 outputs
Outputs of similar age
#47,289
of 195,516 outputs
Outputs of similar age from BMC Bioinformatics
#33
of 110 outputs
Altmetric has tracked 22,711,645 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 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 70% 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 195,516 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 75% of its contemporaries.
We're also able to compare this research output to 110 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 70% of its contemporaries.