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16SPIP: a comprehensive analysis pipeline for rapid pathogen detection in clinical samples based on 16S metagenomic sequencing

Overview of attention for article published in BMC Bioinformatics, December 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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63 Mendeley
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Title
16SPIP: a comprehensive analysis pipeline for rapid pathogen detection in clinical samples based on 16S metagenomic sequencing
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1975-3
Pubmed ID
Authors

Jiaojiao Miao, Na Han, Yujun Qiang, Tingting Zhang, Xiuwen Li, Wen Zhang

Abstract

Pathogen detection in clinical samples based on 16S metagenomic sequencing technology in microbiology laboratories is an important strategy for clinical diagnosis, public health surveillance, and investigations of outbreaks. However, the implementation of the technology is limited by its accuracy and the time required for bioinformatics analysis. Therefore, a simple, standardized, and rapid analysis pipeline from the receipt of clinical samples to the generation of a test report is needed to increase the use of metagenomic analyses in clinical settings. We developed a comprehensive bioinformatics analysis pipeline for the identification of pathogens in clinical samples based on 16S metagenomic sequencing data, named 16SPIP. This pipeline offers two analysis modes (fast and sensitive mode) for the rapid conversion of clinical 16S metagenomic data to test reports for pathogen detection. The pipeline includes tools for data conversion, quality control, merging of paired-end reads, alignment, and pathogen identification. We validated the feasibility and accuracy of the pipeline using a combination of culture and whole-genome shotgun (WGS) metagenomic analyses. 16SPIP may be effective for the analysis of 16S metagenomic sequencing data for real-time, rapid, and unbiased pathogen detection in clinical samples.

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X Demographics

The data shown below were collected from the profiles of 5 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 21%
Student > Bachelor 11 17%
Student > Doctoral Student 6 10%
Student > Ph. D. Student 5 8%
Student > Master 5 8%
Other 9 14%
Unknown 14 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 22%
Biochemistry, Genetics and Molecular Biology 13 21%
Environmental Science 5 8%
Engineering 4 6%
Computer Science 2 3%
Other 8 13%
Unknown 17 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 17 March 2022.
All research outputs
#4,588,019
of 23,347,114 outputs
Outputs from BMC Bioinformatics
#1,698
of 7,391 outputs
Outputs of similar age
#99,175
of 443,848 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 143 outputs
Altmetric has tracked 23,347,114 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,391 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 76% 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 443,848 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 77% of its contemporaries.
We're also able to compare this research output to 143 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.