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The substitution rate of HIV-1 subtypes: a genomic approach

Overview of attention for article published in Virus Evolution, October 2017
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)

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4 tweeters

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19 Mendeley
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The substitution rate of HIV-1 subtypes: a genomic approach
Published in
Virus Evolution, October 2017
DOI 10.1093/ve/vex029
Pubmed ID

Juan Ángel Patiño-Galindo, Fernando González-Candelas


HIV-1M causes most infections in the AIDS pandemic. Its genetic diversity is defined by nine pure subtypes and more than sixty recombinant forms. We have performed a comparative analysis of the evolutionary rate of five pure subtypes (A1, B, C, D, and G) and two circulating recombinant forms (CRF01_AE and CRF02 AG) using data obtained from nearly complete genome coding sequences. Times to the most recent common ancestor (tMRCA) and substitution rates of these HIV genomes, and their genomic partitions, were estimated by Bayesian coalescent analyses. Genomic substitution rate estimates were compared between the HIV-1 datasets analyzed by means of randomization tests. Significant differences in the rate of evolution were found between subtypes, with subtypes C and A1 and CRF01_AE displaying the highest rates. On the other hand, CRF02_AG and subtype D were the slowest evolving types. Using a different molecular clock model for each genomic partition led to more precise tMRCA estimates than when linking the same clock along the HIV genome. Overall, the earliest tMRCA corresponded to subtype A1 (median = 1941, 95% HPD = 1943-55), whereas the most recent tMRCA corresponded to subtype G and CRF01_AE subset 3 (median = 1971, 95% HPD = 1967-75 and median = 1972, 95% HPD = 1970-75, respectively). These results suggest that both biological and epidemiological differences among HIV-1M subtypes are reflected in their evolutionary dynamics. The estimates obtained for tMRCAs and substitution rates provide information that can be used as prior distributions in future Bayesian coalescent analyses of specific HIV-1 subtypes/CRFs and genes.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 21%
Student > Bachelor 3 16%
Researcher 3 16%
Student > Doctoral Student 3 16%
Student > Ph. D. Student 2 11%
Other 4 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 37%
Biochemistry, Genetics and Molecular Biology 4 21%
Medicine and Dentistry 3 16%
Mathematics 1 5%
Unspecified 1 5%
Other 3 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 11 November 2017.
All research outputs
of 12,124,842 outputs
Outputs from Virus Evolution
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Outputs of similar age
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Outputs of similar age from Virus Evolution
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Altmetric has tracked 12,124,842 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 157 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one is in the 29th percentile – i.e., 29% 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 282,969 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 52% 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