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Optimizing illumina next-generation sequencing library preparation for extremely at-biased genomes

Overview of attention for article published in BMC Genomics, January 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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
8 tweeters
patent
13 patents

Citations

dimensions_citation
505 Dimensions

Readers on

mendeley
424 Mendeley
citeulike
9 CiteULike
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Title
Optimizing illumina next-generation sequencing library preparation for extremely at-biased genomes
Published in
BMC Genomics, January 2012
DOI 10.1186/1471-2164-13-1
Pubmed ID
Authors

Samuel O Oyola, Thomas D Otto, Yong Gu, Gareth Maslen, Magnus Manske, Susana Campino, Daniel J Turner, Bronwyn MacInnis, Dominic P Kwiatkowski, Harold P Swerdlow, Michael A Quail

Abstract

Massively parallel sequencing technology is revolutionizing approaches to genomic and genetic research. Since its advent, the scale and efficiency of Next-Generation Sequencing (NGS) has rapidly improved. In spite of this success, sequencing genomes or genomic regions with extremely biased base composition is still a great challenge to the currently available NGS platforms. The genomes of some important pathogenic organisms like Plasmodium falciparum (high AT content) and Mycobacterium tuberculosis (high GC content) display extremes of base composition. The standard library preparation procedures that employ PCR amplification have been shown to cause uneven read coverage particularly across AT and GC rich regions, leading to problems in genome assembly and variation analyses. Alternative library-preparation approaches that omit PCR amplification require large quantities of starting material and hence are not suitable for small amounts of DNA/RNA such as those from clinical isolates. We have developed and optimized library-preparation procedures suitable for low quantity starting material and tolerant to extremely high AT content sequences.

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 424 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 13 3%
United States 6 1%
Germany 5 1%
Spain 3 <1%
Brazil 2 <1%
Australia 2 <1%
Israel 1 <1%
Sweden 1 <1%
Korea, Republic of 1 <1%
Other 9 2%
Unknown 381 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 126 30%
Student > Ph. D. Student 106 25%
Student > Master 44 10%
Student > Bachelor 31 7%
Other 26 6%
Other 56 13%
Unknown 35 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 227 54%
Biochemistry, Genetics and Molecular Biology 83 20%
Medicine and Dentistry 23 5%
Engineering 7 2%
Immunology and Microbiology 6 1%
Other 31 7%
Unknown 47 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 11 May 2021.
All research outputs
#1,467,977
of 19,293,201 outputs
Outputs from BMC Genomics
#404
of 9,760 outputs
Outputs of similar age
#13,875
of 236,199 outputs
Outputs of similar age from BMC Genomics
#18
of 512 outputs
Altmetric has tracked 19,293,201 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,760 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 95% 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 236,199 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 94% of its contemporaries.
We're also able to compare this research output to 512 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 96% of its contemporaries.