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A comprehensive evaluation of the sl1p pipeline for 16S rRNA gene sequencing analysis

Overview of attention for article published in Microbiome, August 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
A comprehensive evaluation of the sl1p pipeline for 16S rRNA gene sequencing analysis
Published in
Microbiome, August 2017
DOI 10.1186/s40168-017-0314-2
Pubmed ID
Authors

Fiona J. Whelan, Michael G. Surette

Abstract

Advances in next-generation sequencing technologies have allowed for detailed, molecular-based studies of microbial communities such as the human gut, soil, and ocean waters. Sequencing of the 16S rRNA gene, specific to prokaryotes, using universal PCR primers has become a common approach to studying the composition of these microbiota. However, the bioinformatic processing of the resulting millions of DNA sequences can be challenging, and a standardized protocol would aid in reproducible analyses. The short-read library 16S rRNA gene sequencing pipeline (sl1p, pronounced "slip") was designed with the purpose of mitigating this lack of reproducibility by combining pre-existing tools into a computational pipeline. This pipeline automates the processing of raw 16S rRNA gene sequencing data to create human-readable tables, graphs, and figures to make the collected data more readily accessible. Data generated from mock communities were compared using eight OTU clustering algorithms, two taxon assignment approaches, and three 16S rRNA gene reference databases. While all of these algorithms and options are available to sl1p users, through testing with human-associated mock communities, AbundantOTU+, the RDP Classifier, and the Greengenes 2011 reference database were chosen as sl1p's defaults based on their ability to best represent the known input communities. sl1p promotes reproducible research by providing a comprehensive log file, and reduces the computational knowledge needed by the user to process next-generation sequencing data. sl1p is freely available at https://bitbucket.org/fwhelan/sl1p .

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The data shown below were collected from the profiles of 26 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 144 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 144 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 19%
Student > Ph. D. Student 24 17%
Student > Bachelor 18 13%
Student > Master 16 11%
Student > Doctoral Student 8 6%
Other 21 15%
Unknown 29 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 23%
Biochemistry, Genetics and Molecular Biology 25 17%
Medicine and Dentistry 12 8%
Immunology and Microbiology 11 8%
Neuroscience 6 4%
Other 17 12%
Unknown 40 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 19 August 2017.
All research outputs
#2,316,168
of 25,654,566 outputs
Outputs from Microbiome
#922
of 1,787 outputs
Outputs of similar age
#42,616
of 327,975 outputs
Outputs of similar age from Microbiome
#40
of 63 outputs
Altmetric has tracked 25,654,566 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,787 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 37.9. This one is in the 48th percentile – i.e., 48% 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 327,975 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 86% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.