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Better primer design for metagenomics applications by increasing taxonomic distinguishability

Overview of attention for article published in BMC Proceedings, December 2013
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Title
Better primer design for metagenomics applications by increasing taxonomic distinguishability
Published in
BMC Proceedings, December 2013
DOI 10.1186/1753-6561-7-s7-s4
Pubmed ID
Authors

Melita Jaric, Jonathan Segal, Eugenia Silva-Herzog, Lisa Schneper, Kalai Mathee, Giri Narasimhan

Abstract

Current methods of understanding microbiome composition and structure rely on accurately estimating the number of distinct species and their relative abundance. Most of these methods require an efficient PCR whose forward and reverse primers bind well to the same, large number of identifiable species, and produce amplicons that are unique. It is therefore not surprising that currently used universal primers designed many years ago are not as efficient and fail to bind to recently cataloged species. We propose an automated general method of designing PCR primer pairs that abide by primer design rules and uses current sequence database as input. Since the method is automated, primers can be designed for targeted microbial species or updated as species are added or deleted from the database. In silico experiments and laboratory experiments confirm the efficacy of the newly designed primers for metagenomics applications.

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The data shown below were collected from the profile of 1 X user 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 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
Sweden 1 1%
India 1 1%
United Kingdom 1 1%
Philippines 1 1%
Unknown 89 94%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 21 22%
Student > Ph. D. Student 19 20%
Researcher 17 18%
Student > Master 10 11%
Student > Doctoral Student 7 7%
Other 8 8%
Unknown 13 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 40%
Biochemistry, Genetics and Molecular Biology 19 20%
Environmental Science 5 5%
Chemistry 5 5%
Immunology and Microbiology 5 5%
Other 10 11%
Unknown 13 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 February 2014.
All research outputs
#15,298,293
of 22,751,628 outputs
Outputs from BMC Proceedings
#209
of 374 outputs
Outputs of similar age
#191,472
of 306,107 outputs
Outputs of similar age from BMC Proceedings
#16
of 20 outputs
Altmetric has tracked 22,751,628 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 374 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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