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Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data

Overview of attention for article published in Microbiome, May 2015
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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 (87th percentile)

Mentioned by

25 tweeters


134 Dimensions

Readers on

260 Mendeley
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Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data
Published in
Microbiome, May 2015
DOI 10.1186/s40168-015-0083-8
Pubmed ID

Jake Jervis-Bardy, Lex E X Leong, Shashikanth Marri, Renee J Smith, Jocelyn M Choo, Heidi C Smith-Vaughan, Elizabeth Nosworthy, Peter S Morris, Stephen O’Leary, Geraint B Rogers, Robyn L Marsh


The rapid expansion of 16S rRNA gene sequencing in challenging clinical contexts has resulted in a growing body of literature of variable quality. To a large extent, this is due to a failure to address spurious signal that is characteristic of samples with low levels of bacteria and high levels of non-bacterial DNA. We have developed a workflow based on the paired-end read Illumina MiSeq-based approach, which enables significant improvement in data quality, post-sequencing. We demonstrate the efficacy of this methodology through its application to paediatric upper-respiratory samples from several anatomical sites. A workflow for processing sequence data was developed based on commonly available tools. Data generated from different sample types showed a marked variation in levels of non-bacterial signal and 'contaminant' bacterial reads. Significant differences in the ability of reference databases to accurately assign identity to operational taxonomic units (OTU) were observed. Three OTU-picking strategies were trialled as follows: de novo, open-reference and closed-reference, with open-reference performing substantially better. Relative abundance of OTUs identified as potential reagent contamination showed a strong inverse correlation with amplicon concentration allowing their objective removal. The removal of the spurious signal showed the greatest improvement in sample types typically containing low levels of bacteria and high levels of human DNA. A substantial impact of pre-filtering data and spurious signal removal was demonstrated by principal coordinate and co-occurrence analysis. For example, analysis of taxon co-occurrence in adenoid swab and middle ear fluid samples indicated that failure to remove the spurious signal resulted in the inclusion of six out of eleven bacterial genera that accounted for 80% of similarity between the sample types. The application of the presented workflow to a set of challenging clinical samples demonstrates its utility in removing the spurious signal from the dataset, allowing clinical insight to be derived from what would otherwise be highly misleading output. While other approaches could potentially achieve similar improvements, the methodology employed here represents an accessible means to exclude the signal from contamination and other artefacts.

Twitter Demographics

The data shown below were collected from the profiles of 25 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 260 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Canada 3 1%
Argentina 2 <1%
Italy 1 <1%
Ghana 1 <1%
Germany 1 <1%
France 1 <1%
Thailand 1 <1%
United Kingdom 1 <1%
Other 0 0%
Unknown 245 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 67 26%
Student > Ph. D. Student 65 25%
Student > Master 34 13%
Student > Bachelor 17 7%
Student > Doctoral Student 16 6%
Other 33 13%
Unknown 28 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 94 36%
Biochemistry, Genetics and Molecular Biology 39 15%
Immunology and Microbiology 32 12%
Medicine and Dentistry 22 8%
Engineering 7 3%
Other 23 9%
Unknown 43 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 29 March 2017.
All research outputs
of 21,144,390 outputs
Outputs from Microbiome
of 1,271 outputs
Outputs of similar age
of 244,399 outputs
Outputs of similar age from Microbiome
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Altmetric has tracked 21,144,390 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,271 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.8. This one is in the 33rd percentile – i.e., 33% 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 244,399 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them