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Large-scale analysis of the evolutionary histories of phosphorylation motifs in the human genome

Overview of attention for article published in Giga Science, May 2015
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  • Good Attention Score compared to outputs of the same age (74th percentile)

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7 X users
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1 peer review site
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1 Facebook page
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1 Google+ user

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22 Mendeley
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Title
Large-scale analysis of the evolutionary histories of phosphorylation motifs in the human genome
Published in
Giga Science, May 2015
DOI 10.1186/s13742-015-0057-6
Pubmed ID
Authors

Hisayoshi Yoshizaki, Shujiro Okuda

Abstract

Protein phosphorylation is a post-translational modification that is essential for a wide range of eukaryotic physiological processes, such as transcription, cytoskeletal regulation, cell metabolism, and signal transduction. Although more than 200,000 phosphorylation sites have been reported in the human genome, the physiological roles of most remain unknown. In this study, we provide some useful datasets for the assessment of functional phosphorylation signaling using a comparative genome analysis of phosphorylation motifs. We described the evolutionary patterns of conservation of these and comparative genomic data for 93,101 phosphosites and 1,003,756 potential phosphosites in human phosphomotifs, using 178 phosphomotifs identified in a previous study that occupied 69% of known phosphosites in public databases. Comparative genomic analyses were performed using genomes from nine species from yeast to humans. Here we provide an overview of the evolutionary patterns of phosphomotif acquisition and indicate the dependence on motif structures. Using the data from our previous study, we describe the interaction networks of phosphoproteins, identify the kinase substrates associated with phosphoproteins, and perform gene ontology enrichment analyses. In addition, we show how this dataset can help to elucidate the function of phosphomotifs. Our characterizations of motif structures and assessments of evolutionary conservation of phosphosites reveal physiological roles of unreported phosphosites. Thus, interactions between protein groups that share motifs are likely to be helpful for inferring kinase-substrate interaction networks. Our computational methods can be used to elucidate the relationships between phosphorylation signaling and cellular functions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 5%
South Africa 1 5%
Brazil 1 5%
Unknown 19 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 41%
Student > Master 4 18%
Researcher 3 14%
Student > Bachelor 1 5%
Lecturer > Senior Lecturer 1 5%
Other 2 9%
Unknown 2 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 36%
Agricultural and Biological Sciences 4 18%
Chemistry 3 14%
Computer Science 2 9%
Neuroscience 1 5%
Other 1 5%
Unknown 3 14%
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 09 May 2015.
All research outputs
#6,373,631
of 25,374,647 outputs
Outputs from Giga Science
#874
of 1,168 outputs
Outputs of similar age
#69,688
of 279,099 outputs
Outputs of similar age from Giga Science
#15
of 16 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 74th percentile.
So far Altmetric has tracked 1,168 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one is in the 24th percentile – i.e., 24% 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 279,099 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 74% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.