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Hybrid selection for sequencing pathogen genomes from clinical samples

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 (86th percentile)

Mentioned by

twitter
6 tweeters
patent
3 patents

Citations

dimensions_citation
83 Dimensions

Readers on

mendeley
117 Mendeley
citeulike
7 CiteULike
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Title
Hybrid selection for sequencing pathogen genomes from clinical samples
Published in
Genome Biology (Online Edition), January 2011
DOI 10.1186/gb-2011-12-8-r73
Pubmed ID
Authors

Alexandre Melnikov, Kevin Galinsky, Peter Rogov, Timothy Fennell, Daria Tyne, Carsten Russ, Rachel Daniels, Kayla G Barnes, James Bochicchio, Daouda Ndiaye, Papa D Sene, Dyann F Wirth, Chad Nusbaum, Sarah K Volkman, Bruce W Birren, Andreas Gnirke, Daniel E Neafsey

Abstract

We have adapted a solution hybrid selection protocol to enrich pathogen DNA in clinical samples dominated by human genetic material. Using mock mixtures of human and Plasmodium falciparum malaria parasite DNA as well as clinical samples from infected patients, we demonstrate an average of approximately 40-fold enrichment of parasite DNA after hybrid selection. This approach will enable efficient genome sequencing of pathogens from clinical samples, as well as sequencing of endosymbiotic organisms such as Wolbachia that live inside diverse metazoan phyla.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 5%
United Kingdom 5 4%
Brazil 2 2%
Mexico 1 <1%
Pakistan 1 <1%
Sweden 1 <1%
Denmark 1 <1%
China 1 <1%
Germany 1 <1%
Other 2 2%
Unknown 96 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 33%
Student > Ph. D. Student 35 30%
Student > Master 13 11%
Student > Doctoral Student 7 6%
Professor 6 5%
Other 11 9%
Unknown 6 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 79 68%
Biochemistry, Genetics and Molecular Biology 10 9%
Medicine and Dentistry 8 7%
Mathematics 2 2%
Computer Science 2 2%
Other 7 6%
Unknown 9 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 01 April 2021.
All research outputs
#2,484,246
of 18,054,508 outputs
Outputs from Genome Biology (Online Edition)
#1,968
of 3,698 outputs
Outputs of similar age
#13,539
of 99,176 outputs
Outputs of similar age from Genome Biology (Online Edition)
#3
of 4 outputs
Altmetric has tracked 18,054,508 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,698 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.7. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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 99,176 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 4 others from the same source and published within six weeks on either side of this one.