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Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries

Overview of attention for article published in Genome Biology (Online Edition), January 2011
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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 (91st percentile)

Mentioned by

twitter
8 tweeters
patent
3 patents
wikipedia
2 Wikipedia pages
f1000
1 research highlight platform

Citations

dimensions_citation
687 Dimensions

Readers on

mendeley
1403 Mendeley
citeulike
25 CiteULike
connotea
3 Connotea
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Title
Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries
Published in
Genome Biology (Online Edition), January 2011
DOI 10.1186/gb-2011-12-2-r18
Pubmed ID
Authors

Daniel Aird, Michael G Ross, Wei-Sheng Chen, Maxwell Danielsson, Timothy Fennell, Carsten Russ, David B Jaffe, Chad Nusbaum, Andreas Gnirke

Abstract

Despite the ever-increasing output of Illumina sequencing data, loci with extreme base compositions are often under-represented or absent. To evaluate sources of base-composition bias, we traced genomic sequences ranging from 6% to 90% GC through the process by quantitative PCR. We identified PCR during library preparation as a principal source of bias and optimized the conditions. Our improved protocol significantly reduces amplification bias and minimizes the previously severe effects of PCR instrument and temperature ramp rate.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1,403 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 39 3%
United Kingdom 24 2%
Germany 16 1%
Spain 9 <1%
Brazil 7 <1%
Canada 6 <1%
France 6 <1%
Denmark 5 <1%
Sweden 3 <1%
Other 33 2%
Unknown 1255 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 415 30%
Researcher 384 27%
Student > Master 163 12%
Student > Bachelor 114 8%
Other 59 4%
Other 184 13%
Unknown 84 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 784 56%
Biochemistry, Genetics and Molecular Biology 273 19%
Computer Science 43 3%
Medicine and Dentistry 41 3%
Environmental Science 34 2%
Other 124 9%
Unknown 104 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 26 March 2019.
All research outputs
#1,154,894
of 14,411,276 outputs
Outputs from Genome Biology (Online Edition)
#1,194
of 3,183 outputs
Outputs of similar age
#7,432
of 92,572 outputs
Outputs of similar age from Genome Biology (Online Edition)
#2
of 3 outputs
Altmetric has tracked 14,411,276 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,183 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.6. This one has gotten more attention than average, scoring higher than 62% 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 92,572 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.