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Profiles of low complexity regions in Apicomplexa

Overview of attention for article published in BMC Ecology and Evolution, February 2016
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

blogs
1 blog

Citations

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21 Dimensions

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18 Mendeley
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Title
Profiles of low complexity regions in Apicomplexa
Published in
BMC Ecology and Evolution, February 2016
DOI 10.1186/s12862-016-0625-0
Pubmed ID
Authors

Fabia U. Battistuzzi, Kristan A. Schneider, Matthew K. Spencer, David Fisher, Sophia Chaudhry, Ananias A. Escalante

Abstract

Low complexity regions (LCRs) are a ubiquitous feature in genomes and yet their evolutionary history and functional roles are unclear. Previous studies have shown contrasting evidence in favor of both neutral and selective mechanisms of evolution for different sets of LCRs suggesting that modes of identification of these regions may play a role in our ability to discern their evolutionary history. To further investigate this issue, we used a multiple threshold approach to identify species-specific profiles of proteome complexity and, by comparing properties of these sets, determine the influence that starting parameters have on evolutionary inferences. We find that, although qualitatively similar, quantitatively each species has a unique LCR profile which represents the frequency of these regions within each genome. Inferences based on these profiles are more accurate in comparative analyses of genome complexity as they allow to determine the relative complexity of multiple genomes as well as the type of repetitiveness that is most common in each. Based on the multiple threshold LCR sets obtained, we identified predominant evolutionary mechanisms at different complexity levels, which show neutral mechanisms acting on highly repetitive LCRs (e.g., homopolymers) and selective forces becoming more important as heterogeneity of the LCRs increases. Our results show how inferences based on LCRs are influenced by the parameters used to identify these regions. Sets of LCRs are heterogeneous aggregates of regions that include homo- and heteropolymers and, as such, evolve according to different mechanisms. LCR profiles provide a new way to investigate genome complexity across species and to determine the driving mechanism of their evolution.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 17%
Student > Ph. D. Student 3 17%
Student > Bachelor 2 11%
Professor 2 11%
Student > Doctoral Student 1 6%
Other 0 0%
Unknown 7 39%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 44%
Agricultural and Biological Sciences 1 6%
Immunology and Microbiology 1 6%
Engineering 1 6%
Unknown 7 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 02 March 2016.
All research outputs
#6,748,809
of 25,374,647 outputs
Outputs from BMC Ecology and Evolution
#1,499
of 3,714 outputs
Outputs of similar age
#87,128
of 312,043 outputs
Outputs of similar age from BMC Ecology and Evolution
#25
of 71 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 3,714 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 59% 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 312,043 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 72% of its contemporaries.
We're also able to compare this research output to 71 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 64% of its contemporaries.