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Phylogenetically typing bacterial strains from partial SNP genotypes observed from direct sequencing of clinical specimen metagenomic data

Overview of attention for article published in Genome Medicine, June 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 (80th percentile)

Mentioned by

twitter
13 tweeters

Citations

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34 Dimensions

Readers on

mendeley
85 Mendeley
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Title
Phylogenetically typing bacterial strains from partial SNP genotypes observed from direct sequencing of clinical specimen metagenomic data
Published in
Genome Medicine, June 2015
DOI 10.1186/s13073-015-0176-9
Pubmed ID
Authors

Jason W. Sahl, James M. Schupp, David A. Rasko, Rebecca E. Colman, Jeffrey T. Foster, Paul Keim

Abstract

We describe an approach for genotyping bacterial strains from low coverage genome datasets, including metagenomic data from complex samples. Sequence reads from unknown samples are aligned to a reference genome where the allele states of known SNPs are determined. The Whole Genome Focused Array SNP Typing (WG-FAST) pipeline can identify unknown strains with much less read data than is needed for genome assembly. To test WG-FAST, we resampled SNPs from real samples to understand the relationship between low coverage metagenomic data and accurate phylogenetic placement. WG-FAST can be downloaded from https://github.com/jasonsahl/wgfast.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Portugal 1 1%
Sweden 1 1%
Brazil 1 1%
Russia 1 1%
Denmark 1 1%
Unknown 77 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 29%
Researcher 15 18%
Student > Bachelor 11 13%
Student > Doctoral Student 7 8%
Student > Master 7 8%
Other 10 12%
Unknown 10 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 38%
Biochemistry, Genetics and Molecular Biology 20 24%
Computer Science 8 9%
Medicine and Dentistry 4 5%
Engineering 3 4%
Other 8 9%
Unknown 10 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 04 January 2016.
All research outputs
#3,489,582
of 20,037,010 outputs
Outputs from Genome Medicine
#718
of 1,301 outputs
Outputs of similar age
#47,745
of 247,884 outputs
Outputs of similar age from Genome Medicine
#1
of 2 outputs
Altmetric has tracked 20,037,010 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,301 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.5. This one is in the 44th percentile – i.e., 44% 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 247,884 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 80% of its contemporaries.
We're also able to compare this research output to 2 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