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Steps to ensure accuracy in genotype and SNP calling from Illumina sequencing data

Overview of attention for article published in BMC Genomics, December 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

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15 X users

Citations

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98 Dimensions

Readers on

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359 Mendeley
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6 CiteULike
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Title
Steps to ensure accuracy in genotype and SNP calling from Illumina sequencing data
Published in
BMC Genomics, December 2012
DOI 10.1186/1471-2164-13-s8-s8
Pubmed ID
Authors

Qi Liu, Yan Guo, Jiang Li, Jirong Long, Bing Zhang, Yu Shyr

Abstract

Accurate calling of SNPs and genotypes from next-generation sequencing data is an essential prerequisite for most human genetics studies. A number of computational steps are required or recommended when translating the raw sequencing data into the final calls. However, whether each step does contribute to the performance of variant calling and how it affects the accuracy still remain unclear, making it difficult to select and arrange appropriate steps to derive high quality variants from different sequencing data. In this study, we made a systematic assessment of the relative contribution of each step to the accuracy of variant calling from Illumina DNA sequencing data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 359 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 15 4%
France 3 <1%
Italy 2 <1%
Sweden 2 <1%
Brazil 2 <1%
Netherlands 2 <1%
New Zealand 2 <1%
United Kingdom 2 <1%
Norway 1 <1%
Other 7 2%
Unknown 321 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 102 28%
Student > Ph. D. Student 85 24%
Student > Master 53 15%
Student > Doctoral Student 18 5%
Student > Bachelor 18 5%
Other 53 15%
Unknown 30 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 203 57%
Biochemistry, Genetics and Molecular Biology 73 20%
Computer Science 19 5%
Medicine and Dentistry 6 2%
Environmental Science 5 1%
Other 16 4%
Unknown 37 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 08 October 2019.
All research outputs
#3,540,634
of 22,691,736 outputs
Outputs from BMC Genomics
#1,348
of 10,616 outputs
Outputs of similar age
#34,396
of 261,298 outputs
Outputs of similar age from BMC Genomics
#58
of 382 outputs
Altmetric has tracked 22,691,736 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,616 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 87% 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 261,298 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 86% of its contemporaries.
We're also able to compare this research output to 382 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.