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Evaluation of PacBio sequencing for full-length bacterial 16S rRNA gene classification

Overview of attention for article published in BMC Microbiology, November 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#30 of 2,431)
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

blogs
1 blog
twitter
55 tweeters
googleplus
1 Google+ user

Citations

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74 Dimensions

Readers on

mendeley
206 Mendeley
citeulike
2 CiteULike
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Title
Evaluation of PacBio sequencing for full-length bacterial 16S rRNA gene classification
Published in
BMC Microbiology, November 2016
DOI 10.1186/s12866-016-0891-4
Pubmed ID
Authors

Josef Wagner, Paul Coupland, Hilary P. Browne, Trevor D. Lawley, Suzanna C. Francis, Julian Parkhill

Abstract

Currently, bacterial 16S rRNA gene analyses are based on sequencing of individual variable regions of the 16S rRNA gene (Kozich, et al Appl Environ Microbiol 79:5112-5120, 2013).This short read approach can introduce biases. Thus, full-length bacterial 16S rRNA gene sequencing is needed to reduced biases. A new alternative for full-length bacterial 16S rRNA gene sequencing is offered by PacBio single molecule, real-time (SMRT) technology. The aim of our study was to validate PacBio P6 sequencing chemistry using three approaches: 1) sequencing the full-length bacterial 16S rRNA gene from a single bacterial species Staphylococcus aureus to analyze error modes and to optimize the bioinformatics pipeline; 2) sequencing the full-length bacterial 16S rRNA gene from a pool of 50 different bacterial colonies from human stool samples to compare with full-length bacterial 16S rRNA capillary sequence; and 3) sequencing the full-length bacterial 16S rRNA genes from 11 vaginal microbiome samples and compare with in silico selected bacterial 16S rRNA V1V2 gene region and with bacterial 16S rRNA V1V2 gene regions sequenced using the Illumina MiSeq. Our optimized bioinformatics pipeline for PacBio sequence analysis was able to achieve an error rate of 0.007% on the Staphylococcus aureus full-length 16S rRNA gene. Capillary sequencing of the full-length bacterial 16S rRNA gene from the pool of 50 colonies from stool identified 40 bacterial species of which up to 80% could be identified by PacBio full-length bacterial 16S rRNA gene sequencing. Analysis of the human vaginal microbiome using the bacterial 16S rRNA V1V2 gene region on MiSeq generated 129 operational taxonomic units (OTUs) from which 70 species could be identified. For the PacBio, 36,000 sequences from over 58,000 raw reads could be assigned to a barcode, and the in silico selected bacterial 16S rRNA V1V2 gene region generated 154 OTUs grouped into 63 species, of which 62% were shared with the MiSeq dataset. The PacBio full-length bacterial 16S rRNA gene datasets generated 261 OTUs, which were grouped into 52 species, of which 54% were shared with the MiSeq dataset. Alpha diversity index reported a higher diversity in the MiSeq dataset. The PacBio sequencing error rate is now in the same range of the previously widely used Roche 454 sequencing platform and current MiSeq platform. Species-level microbiome analysis revealed some inconsistencies between the full-length bacterial 16S rRNA gene capillary sequencing and PacBio sequencing.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 206 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
United Kingdom 1 <1%
Switzerland 1 <1%
Belgium 1 <1%
Germany 1 <1%
Unknown 198 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 45 22%
Student > Ph. D. Student 43 21%
Student > Master 25 12%
Student > Doctoral Student 17 8%
Student > Bachelor 13 6%
Other 35 17%
Unknown 28 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 64 31%
Biochemistry, Genetics and Molecular Biology 47 23%
Immunology and Microbiology 16 8%
Medicine and Dentistry 9 4%
Environmental Science 8 4%
Other 22 11%
Unknown 40 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 37. 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 11 April 2019.
All research outputs
#602,271
of 15,918,909 outputs
Outputs from BMC Microbiology
#30
of 2,431 outputs
Outputs of similar age
#18,386
of 290,812 outputs
Outputs of similar age from BMC Microbiology
#5
of 265 outputs
Altmetric has tracked 15,918,909 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,431 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 98% 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 290,812 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 265 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 98% of its contemporaries.