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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 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
116 Dimensions

Readers on

mendeley
139 Mendeley
citeulike
1 CiteULike
Summary Twitter Wikipedia Dimensions citations
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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
26450747
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.

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Twitter Demographics

The data shown below were collected from the profiles of 16 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 139 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%
Sweden 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Spain 1 <1%
Mexico 1 <1%
Other 0 0%
Unknown 126 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 27%
Researcher 32 23%
Student > Master 16 12%
Student > Bachelor 9 6%
Student > Doctoral Student 6 4%
Other 20 14%
Unknown 19 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 27%
Biochemistry, Genetics and Molecular Biology 21 15%
Immunology and Microbiology 17 12%
Environmental Science 10 7%
Computer Science 8 6%
Other 15 11%
Unknown 30 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 February 2022.
All research outputs
#2,835,440
of 23,646,998 outputs
Outputs from BMC Bioinformatics
#918
of 7,411 outputs
Outputs of similar age
#39,679
of 279,362 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 138 outputs
Altmetric has tracked 23,646,998 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 7,411 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 279,362 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.

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