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Selection of marker genes for genetic barcoding of microorganisms and binning of metagenomic reads by Barcoder software tools

Overview of attention for article published in BMC Bioinformatics, August 2018
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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)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
1 blog
twitter
9 tweeters

Citations

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1 Dimensions

Readers on

mendeley
47 Mendeley
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Title
Selection of marker genes for genetic barcoding of microorganisms and binning of metagenomic reads by Barcoder software tools
Published in
BMC Bioinformatics, August 2018
DOI 10.1186/s12859-018-2320-1
Pubmed ID
Authors

Adeola M. Rotimi, Rian Pierneef, Oleg N. Reva

Abstract

Metagenomic approaches have revealed the complexity of environmental microbiomes with the advancement in whole genome sequencing displaying a significant level of genetic heterogeneity on the species level. It has become apparent that patterns of superior bioactivity of bacteria applicable in biotechnology as well as the enhanced virulence of pathogens often requires distinguishing between closely related species or sub-species. Current methods for binning of metagenomic reads usually do not allow for identification below the genus level and generally stops at the family level. In this work, an attempt was made to improve metagenomic binning resolution by creating genome specific barcodes based on the core and accessory genomes. This protocol was implemented in novel software tools available for use and download from http://bargene.bi.up.ac.za /. The most abundant barcode genes from the core genomes were found to encode for ribosomal proteins, certain central metabolic genes and ABC transporters. Performance of metabarcode sequences created by this package was evaluated using artificially generated and publically available metagenomic datasets. Furthermore, a program (Barcoding 2.0) was developed to align reads against barcode sequences and thereafter calculate various parameters to score the alignments and the individual barcodes. Taxonomic units were identified in metagenomic samples by comparison of the calculated barcode scores to set cut-off values. In this study, it was found that varying sample sizes, i.e. number of reads in a metagenome and metabarcode lengths, had no significant effect on the sensitivity and specificity of the algorithm. Receiver operating characteristics (ROC) curves were calculated for different taxonomic groups based on the results of identification of the corresponding genomes in artificial metagenomic datasets. The reliability of distinguishing between species of the same genus or family by the program was nearly perfect. The results showed that the novel online tool BarcodeGenerator ( http://bargene.bi.up.ac.za /) is an efficient approach for generating barcode sequences from a set of complete genomes provided by users. Another program, Barcoder 2.0 is available from the same resource to enable an efficient and practical use of metabarcodes for visualization of the distribution of organisms of interest in environmental and clinical samples.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 21%
Student > Bachelor 9 19%
Student > Ph. D. Student 7 15%
Student > Postgraduate 4 9%
Student > Master 4 9%
Other 5 11%
Unknown 8 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 36%
Agricultural and Biological Sciences 12 26%
Immunology and Microbiology 4 9%
Environmental Science 3 6%
Computer Science 1 2%
Other 3 6%
Unknown 7 15%

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 07 October 2018.
All research outputs
#2,423,683
of 18,605,513 outputs
Outputs from BMC Bioinformatics
#919
of 6,404 outputs
Outputs of similar age
#55,319
of 286,954 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 26 outputs
Altmetric has tracked 18,605,513 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 6,404 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 85% 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 286,954 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 26 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 92% of its contemporaries.