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Optimizing PCR primers targeting the bacterial 16S ribosomal RNA gene

Overview of attention for article published in BMC Bioinformatics, September 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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26 X users
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299 Mendeley
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Title
Optimizing PCR primers targeting the bacterial 16S ribosomal RNA gene
Published in
BMC Bioinformatics, September 2018
DOI 10.1186/s12859-018-2360-6
Pubmed ID
Authors

Francesco Sambo, Francesca Finotello, Enrico Lavezzo, Giacomo Baruzzo, Giulia Masi, Elektra Peta, Marco Falda, Stefano Toppo, Luisa Barzon, Barbara Di Camillo

Abstract

Targeted amplicon sequencing of the 16S ribosomal RNA gene is one of the key tools for studying microbial diversity. The accuracy of this approach strongly depends on the choice of primer pairs and, in particular, on the balance between efficiency, specificity and sensitivity in the amplification of the different bacterial 16S sequences contained in a sample. There is thus the need for computational methods to design optimal bacterial 16S primers able to take into account the knowledge provided by the new sequencing technologies. We propose here a computational method for optimizing the choice of primer sets, based on multi-objective optimization, which simultaneously: 1) maximizes efficiency and specificity of target amplification; 2) maximizes the number of different bacterial 16S sequences matched by at least one primer; 3) minimizes the differences in the number of primers matching each bacterial 16S sequence. Our algorithm can be applied to any desired amplicon length without affecting computational performance. The source code of the developed algorithm is released as the mopo16S software tool (Multi-Objective Primer Optimization for 16S experiments) under the GNU General Public License and is available at http://sysbiobig.dei.unipd.it/?q=Software#mopo16S . Results show that our strategy is able to find better primer pairs than the ones available in the literature according to all three optimization criteria. We also experimentally validated three of the primer pairs identified by our method on multiple bacterial species, belonging to different genera and phyla. Results confirm the predicted efficiency and the ability to maximize the number of different bacterial 16S sequences matched by primers.

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The data shown below were collected from the profiles of 26 X users 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 299 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 299 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 49 16%
Researcher 44 15%
Student > Master 37 12%
Student > Ph. D. Student 31 10%
Student > Doctoral Student 10 3%
Other 28 9%
Unknown 100 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 72 24%
Agricultural and Biological Sciences 46 15%
Immunology and Microbiology 19 6%
Medicine and Dentistry 12 4%
Environmental Science 8 3%
Other 38 13%
Unknown 104 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 30 August 2022.
All research outputs
#1,989,062
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#495
of 7,387 outputs
Outputs of similar age
#43,886
of 342,681 outputs
Outputs of similar age from BMC Bioinformatics
#9
of 96 outputs
Altmetric has tracked 23,344,526 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 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 93% 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 342,681 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 87% of its contemporaries.
We're also able to compare this research output to 96 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 91% of its contemporaries.