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Comprehensive benchmarking and ensemble approaches for metagenomic classifiers

Overview of attention for article published in Genome Biology, September 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
twitter
81 X users
wikipedia
1 Wikipedia page
googleplus
2 Google+ users

Citations

dimensions_citation
277 Dimensions

Readers on

mendeley
515 Mendeley
citeulike
4 CiteULike
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Title
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
Published in
Genome Biology, September 2017
DOI 10.1186/s13059-017-1299-7
Pubmed ID
Authors

Alexa B. R. McIntyre, Rachid Ounit, Ebrahim Afshinnekoo, Robert J. Prill, Elizabeth Hénaff, Noah Alexander, Samuel S. Minot, David Danko, Jonathan Foox, Sofia Ahsanuddin, Scott Tighe, Nur A. Hasan, Poorani Subramanian, Kelly Moffat, Shawn Levy, Stefano Lonardi, Nick Greenfield, Rita R. Colwell, Gail L. Rosen, Christopher E. Mason

Abstract

One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

X Demographics

X Demographics

The data shown below were collected from the profiles of 81 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 515 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ukraine 1 <1%
Unknown 514 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 116 23%
Student > Ph. D. Student 113 22%
Student > Master 54 10%
Student > Bachelor 46 9%
Student > Doctoral Student 23 4%
Other 60 12%
Unknown 103 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 143 28%
Biochemistry, Genetics and Molecular Biology 129 25%
Computer Science 47 9%
Immunology and Microbiology 22 4%
Medicine and Dentistry 15 3%
Other 42 8%
Unknown 117 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 58. 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 24 February 2021.
All research outputs
#729,139
of 25,382,440 outputs
Outputs from Genome Biology
#470
of 4,468 outputs
Outputs of similar age
#15,087
of 325,640 outputs
Outputs of similar age from Genome Biology
#11
of 61 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 89% 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 325,640 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 95% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.