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Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin

Overview of attention for article published in Microbiome, May 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 (90th percentile)

Mentioned by

blogs
1 blog
twitter
32 tweeters

Citations

dimensions_citation
1050 Dimensions

Readers on

mendeley
1143 Mendeley
citeulike
1 CiteULike
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Title
Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin
Published in
Microbiome, May 2018
DOI 10.1186/s40168-018-0470-z
Pubmed ID
Authors

Nicholas A. Bokulich, Benjamin D. Kaehler, Jai Ram Rideout, Matthew Dillon, Evan Bolyen, Rob Knight, Gavin A. Huttley, J. Gregory Caporaso

Abstract

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

Twitter Demographics

The data shown below were collected from the profiles of 32 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 1,143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1143 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 253 22%
Researcher 189 17%
Student > Master 182 16%
Student > Bachelor 107 9%
Student > Doctoral Student 63 6%
Other 131 11%
Unknown 218 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 322 28%
Biochemistry, Genetics and Molecular Biology 202 18%
Environmental Science 102 9%
Immunology and Microbiology 62 5%
Medicine and Dentistry 40 3%
Other 138 12%
Unknown 277 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 18 November 2021.
All research outputs
#1,096,594
of 19,496,717 outputs
Outputs from Microbiome
#387
of 1,179 outputs
Outputs of similar age
#28,027
of 292,376 outputs
Outputs of similar age from Microbiome
#1
of 1 outputs
Altmetric has tracked 19,496,717 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,179 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.1. This one has gotten more attention than average, scoring higher than 67% 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 292,376 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 90% 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