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Systematic Analysis of Compositional Order of Proteins Reveals New Characteristics of Biological Functions and a Universal Correlate of Macroevolution

Overview of attention for article published in PLoS Computational Biology, November 2013
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2 X users
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1 Wikipedia page

Citations

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12 Dimensions

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40 Mendeley
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2 CiteULike
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Title
Systematic Analysis of Compositional Order of Proteins Reveals New Characteristics of Biological Functions and a Universal Correlate of Macroevolution
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003346
Pubmed ID
Authors

Erez Persi, David Horn

Abstract

We present a novel analysis of compositional order (CO) based on the occurrence of Frequent amino-acid Triplets (FTs) that appear much more than random in protein sequences. The method captures all types of proteomic compositional order including single amino-acid runs, tandem repeats, periodic structure of motifs and otherwise low complexity amino-acid regions. We introduce new order measures, distinguishing between 'regularity', 'periodicity' and 'vocabulary', to quantify these phenomena and to facilitate the identification of evolutionary effects. Detailed analysis of representative species across the tree-of-life demonstrates that CO proteins exhibit numerous functional enrichments, including a wide repertoire of particular patterns of dependencies on regularity and periodicity. Comparison between human and mouse proteomes further reveals the interplay of CO with evolutionary trends, such as faster substitution rate in mouse leading to decrease of periodicity, while innovation along the human lineage leads to larger regularity. Large-scale analysis of 94 proteomes leads to systematic ordering of all major taxonomic groups according to FT-vocabulary size. This is measured by the count of Different Frequent Triplets (DFT) in proteomes. The latter provides a clear hierarchical delineation of vertebrates, invertebrates, plants, fungi and prokaryotes, with thermophiles showing the lowest level of FT-vocabulary. Among eukaryotes, this ordering correlates with phylogenetic proximity. Interestingly, in all kingdoms CO accumulation in the proteome has universal characteristics. We suggest that CO is a genomic-information correlate of both macroevolution and various protein functions. The results indicate a mechanism of genomic 'innovation' at the peptide level, involved in protein elongation, shaped in a universal manner by mutational and selective forces.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 3%
Israel 1 3%
Unknown 38 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 28%
Researcher 8 20%
Professor > Associate Professor 5 13%
Student > Doctoral Student 4 10%
Student > Master 3 8%
Other 6 15%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 45%
Biochemistry, Genetics and Molecular Biology 9 23%
Computer Science 4 10%
Physics and Astronomy 2 5%
Economics, Econometrics and Finance 1 3%
Other 1 3%
Unknown 5 13%
Attention Score in Context

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 23 August 2020.
All research outputs
#7,959,659
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#5,294
of 8,958 outputs
Outputs of similar age
#86,560
of 315,403 outputs
Outputs of similar age from PLoS Computational Biology
#84
of 146 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 8,958 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 39th percentile – i.e., 39% 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 315,403 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 71% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.