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Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage

Overview of attention for article published in Algorithms for Molecular Biology, March 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#9 of 215)
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

blogs
1 blog
twitter
7 tweeters

Citations

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3 Dimensions

Readers on

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72 Mendeley
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Title
Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
Published in
Algorithms for Molecular Biology, March 2016
DOI 10.1186/s13015-016-0064-x
Pubmed ID
Authors

Jean P. Zukurov, Sieberth do Nascimento-Brito, Angela C. Volpini, Guilherme C. Oliveira, Luiz Mario R. Janini, Fernando Antoneli

Abstract

In this paper we propose a method and discuss its computational implementation as an integrated tool for the analysis of viral genetic diversity on data generated by high-throughput sequencing. The main motivation for this work is to better understand the genetic diversity of viruses with high rates of nucleotide substitution, as HIV-1 and Influenza. Most methods for viral diversity estimation proposed so far are intended to take benefit of the longer reads produced by some next-generation sequencing platforms in order to estimate a population of haplotypes which represent the diversity of the original population. The method proposed here is custom-made to take advantage of the very low error rate and extremely deep coverage per site, which are the main features of some neglected technologies that have not received much attention due to the short length of its reads, which precludes haplotype estimation. This approach allowed us to avoid some hard problems related to haplotype reconstruction (need of long reads, preliminary error filtering and assembly). We propose to measure genetic diversity of a viral population through a family of multinomial probability distributions indexed by the sites of the virus genome, each one representing the distribution of nucleic bases per site. Moreover, the implementation of the method focuses on two main optimization strategies: a read mapping/alignment procedure that aims at the recovery of the maximum possible number of short-reads; the inference of the multinomial parameters in a Bayesian framework with smoothed Dirichlet estimation. The Bayesian approach provides conditional probability distributions for the multinomial parameters allowing one to take into account the prior information of the control experiment and providing a natural way to separate signal from noise, since it automatically furnishes Bayesian confidence intervals and thus avoids the drawbacks of preliminary error filtering. The methods described in this paper have been implemented as an integrated tool called Tanden (Tool for Analysis of Diversity in Viral Populations) and successfully tested on samples obtained from HIV-1 strain NL4-3 (group M, subtype B) cultivations on primary human cell cultures in many distinct viral propagation conditions. Tanden is written in C# (Microsoft), runs on the Windows operating system, and can be downloaded from: http://tanden.url.ph/.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Japan 1 1%
Sweden 1 1%
Unknown 68 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 25%
Student > Ph. D. Student 15 21%
Student > Master 12 17%
Student > Doctoral Student 9 13%
Professor 4 6%
Other 12 17%
Unknown 2 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 38%
Biochemistry, Genetics and Molecular Biology 17 24%
Immunology and Microbiology 8 11%
Medicine and Dentistry 5 7%
Computer Science 4 6%
Other 5 7%
Unknown 6 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 16 March 2016.
All research outputs
#1,286,650
of 14,573,111 outputs
Outputs from Algorithms for Molecular Biology
#9
of 215 outputs
Outputs of similar age
#19,075
of 189,933 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 3 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 215 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done particularly well, scoring higher than 95% 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 189,933 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 3 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