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Improved genome sequencing using an engineered transposase

Overview of attention for article published in BMC Biotechnology, January 2017
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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 (80th percentile)

Mentioned by

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3 tweeters
patent
2 patents

Citations

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21 Dimensions

Readers on

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113 Mendeley
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Title
Improved genome sequencing using an engineered transposase
Published in
BMC Biotechnology, January 2017
DOI 10.1186/s12896-016-0326-1
Pubmed ID
Authors

Amirali Kia, Christian Gloeckner, Trina Osothprarop, Niall Gormley, Erin Bomati, Michelle Stephenson, Igor Goryshin, Molly Min He

Abstract

Next-generation sequencing (NGS) has transformed genomic research by reducing turnaround time and cost. However, no major breakthrough has been made in the upstream library preparation methods until the transposase-based Nextera method was invented. Nextera combines DNA fragmentation and barcoding in a single tube reaction and therefore enables a very fast workflow to sequencing-ready DNA libraries within a couple of hours. When compared to the traditional ligation-based methods, transposed-based Nextera has a slight insertion bias. Here we present the discovery of a mutant transposase (Tn5-059) with a lowered GC insertion bias through protein engineering. We demonstrate Tn5-059 reduces AT dropout and increases uniformity of genome coverage in both bacterial genomes and human genome. We also observe higher library diversity generated by Tn5-059 when compared to Nextera v2 for human exomes, which leads to less sequencing and lower cost per genome. In addition, when used for human exomes, Tn5-059 delivers consistent library insert size over a range of input DNA, allowing up to a tenfold variance from the 50 ng input recommendation. Enhanced DNA input tolerance of Tn5-059 can translate to flexibility and robustness of workflow. DNA input tolerance together with superior uniformity of coverage and lower AT dropouts extend the applications of transposase based library preps. We discuss possible mechanisms of improvements in Tn5-059, and potential advantages of using the new mutant in varieties of applications including microbiome sequencing and chromatin profiling.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 <1%
Switzerland 1 <1%
South Africa 1 <1%
Unknown 110 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 27%
Student > Ph. D. Student 23 20%
Student > Bachelor 14 12%
Student > Master 11 10%
Student > Postgraduate 6 5%
Other 13 12%
Unknown 16 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 29%
Agricultural and Biological Sciences 32 28%
Engineering 7 6%
Medicine and Dentistry 6 5%
Chemistry 4 4%
Other 12 11%
Unknown 19 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 16 February 2021.
All research outputs
#2,943,379
of 17,897,867 outputs
Outputs from BMC Biotechnology
#176
of 874 outputs
Outputs of similar age
#71,684
of 367,618 outputs
Outputs of similar age from BMC Biotechnology
#1
of 1 outputs
Altmetric has tracked 17,897,867 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 874 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one has done well, scoring higher than 79% 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 367,618 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 80% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them