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SPINGO: a rapid species-classifier for microbial amplicon sequences

Overview of attention for article published in BMC Bioinformatics, October 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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

twitter
16 X users
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
125 Dimensions

Readers on

mendeley
145 Mendeley
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1 CiteULike
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Title
SPINGO: a rapid species-classifier for microbial amplicon sequences
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0747-1
Pubmed ID
Authors

Guy Allard, Feargal J. Ryan, Ian B. Jeffery, Marcus J. Claesson

Abstract

Taxonomic classification is a corner stone for the characterisation and comparison of microbial communities. Currently, most existing methods are either slow, restricted to specific communities, highly sensitive to taxonomic inconsistencies, or limited to genus level classification. As crucial microbiota information is hinging on high-level resolution it is imperative to increase taxonomic resolution to species level wherever possible. In response to this need we developed SPINGO, a flexible and stand-alone software dedicated to high-resolution assignment of sequences to species level using partial 16S rRNA gene sequences from any environment. SPINGO compares favourably to other methods in terms of classification accuracy, and is as fast or faster than those that have higher error rates. As a demonstration of its flexibility for other types of target genes we successfully applied SPINGO also on cpn60 amplicon sequences. SPINGO is an accurate, flexible and fast method for low-level taxonomic assignment. This combination is becoming increasingly important for rapid and accurate processing of amplicon data generated by newer next generation sequencing technologies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 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 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 2%
France 2 1%
United States 2 1%
Ireland 1 <1%
United Kingdom 1 <1%
Sweden 1 <1%
Spain 1 <1%
Canada 1 <1%
Unknown 133 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 25%
Researcher 31 21%
Student > Master 18 12%
Student > Bachelor 11 8%
Student > Doctoral Student 6 4%
Other 19 13%
Unknown 24 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 26%
Biochemistry, Genetics and Molecular Biology 23 16%
Immunology and Microbiology 17 12%
Environmental Science 10 7%
Computer Science 8 6%
Other 13 9%
Unknown 36 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 03 June 2022.
All research outputs
#3,161,674
of 26,017,215 outputs
Outputs from BMC Bioinformatics
#955
of 7,793 outputs
Outputs of similar age
#41,216
of 293,817 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 138 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,793 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done well, scoring higher than 87% 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 293,817 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 85% of its contemporaries.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.