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Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review

Overview of attention for article published in BMC Medicine, March 2016
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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 (89th percentile)

Mentioned by

blogs
1 blog
twitter
17 tweeters

Citations

dimensions_citation
57 Dimensions

Readers on

mendeley
123 Mendeley
Title
Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review
Published in
BMC Medicine, March 2016
DOI 10.1186/s12916-016-0566-x
Pubmed ID
Authors

Hollie-Ann Hatherell, Caroline Colijn, Helen R. Stagg, Charlotte Jackson, Joanne R. Winter, Ibrahim Abubakar

Abstract

Whole genome sequencing (WGS) is becoming an important part of epidemiological investigations of infectious diseases due to greater resolution and cost reductions compared to traditional typing approaches. Many public health and clinical teams will increasingly use WGS to investigate clusters of potential pathogen transmission, making it crucial to understand the benefits and assumptions of the analytical methods for investigating the data. We aimed to understand how different approaches affect inferences of transmission dynamics and outline limitations of the methods. We comprehensively searched electronic databases for studies that presented methods used to interpret WGS data for investigating tuberculosis (TB) transmission. Two authors independently selected studies for inclusion and extracted data. Due to considerable methodological heterogeneity between studies, we present summary data with accompanying narrative synthesis rather than pooled analyses. Twenty-five studies met our inclusion criteria. Despite the range of interpretation tools, the usefulness of WGS data in understanding TB transmission often depends on the amount of genetic diversity in the setting. Where diversity is small, distinguishing re-infections from relapses may be impossible; interpretation may be aided by the use of epidemiological data, examining minor variants and deep sequencing. Conversely, when within-host diversity is large, due to genetic hitchhiking or co-infection of two dissimilar strains, it is critical to understand how it arose. Greater understanding of microevolution and mixed infection will enhance interpretation of WGS data. As sequencing studies have sampled more intensely and integrated multiple sources of information, the understanding of TB transmission and diversity has grown, but there is still much to be learnt about the origins of diversity that will affect inferences from these data. Public health teams and researchers should combine epidemiological, clinical and WGS data to strengthen investigations of transmission.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Mexico 1 <1%
Unknown 121 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 24 20%
Researcher 24 20%
Unspecified 23 19%
Student > Ph. D. Student 17 14%
Student > Doctoral Student 8 7%
Other 27 22%
Readers by discipline Count As %
Unspecified 30 24%
Medicine and Dentistry 26 21%
Agricultural and Biological Sciences 24 20%
Biochemistry, Genetics and Molecular Biology 18 15%
Immunology and Microbiology 10 8%
Other 15 12%

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 01 August 2017.
All research outputs
#903,217
of 12,640,996 outputs
Outputs from BMC Medicine
#766
of 2,030 outputs
Outputs of similar age
#28,250
of 264,489 outputs
Outputs of similar age from BMC Medicine
#1
of 1 outputs
Altmetric has tracked 12,640,996 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,030 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.0. This one has gotten more attention than average, scoring higher than 62% 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 264,489 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 89% 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