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Inference of genetic relatedness between viral quasispecies from sequencing data

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

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
9 tweeters

Citations

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19 Dimensions

Readers on

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17 Mendeley
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Title
Inference of genetic relatedness between viral quasispecies from sequencing data
Published in
BMC Genomics, December 2017
DOI 10.1186/s12864-017-4274-5
Pubmed ID
Authors

Olga Glebova, Sergey Knyazev, Andrew Melnyk, Alexander Artyomenko, Yury Khudyakov, Alex Zelikovsky, Pavel Skums

Abstract

RNA viruses such as HCV and HIV mutate at extremely high rates, and as a result, they exist in infected hosts as populations of genetically related variants. Recent advances in sequencing technologies make possible to identify such populations at great depth. In particular, these technologies provide new opportunities for inference of relatedness between viral samples, identification of transmission clusters and sources of infection, which are crucial tasks for viral outbreaks investigations. We present (i) an evolutionary simulation algorithm Viral Outbreak InferenCE (VOICE) inferring genetic relatedness, (ii) an algorithm MinDistB detecting possible transmission using minimal distances between intra-host viral populations and sizes of their relative borders, and (iii) a non-parametric recursive clustering algorithm Relatedness Depth (ReD) analyzing clusters' structure to infer possible transmissions and their directions. All proposed algorithms were validated using real sequencing data from HCV outbreaks. All algorithms are applicable to the analysis of outbreaks of highly heterogeneous RNA viruses. Our experimental validation shows that they can successfully identify genetic relatedness between viral populations, as well as infer transmission clusters and outbreak sources.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 35%
Student > Master 2 12%
Student > Ph. D. Student 2 12%
Professor 1 6%
Student > Bachelor 1 6%
Other 2 12%
Unknown 3 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 24%
Agricultural and Biological Sciences 3 18%
Medicine and Dentistry 2 12%
Mathematics 1 6%
Computer Science 1 6%
Other 2 12%
Unknown 4 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 July 2018.
All research outputs
#5,571,287
of 18,846,097 outputs
Outputs from BMC Genomics
#2,723
of 9,641 outputs
Outputs of similar age
#137,360
of 425,992 outputs
Outputs of similar age from BMC Genomics
#244
of 827 outputs
Altmetric has tracked 18,846,097 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 9,641 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 71% 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 425,992 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 827 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.