↓ Skip to main content

Comparison of phosphorylation patterns across eukaryotes by discriminative N-gram analysis

Overview of attention for article published in BMC Bioinformatics, July 2015
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
4 X users

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
15 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Comparison of phosphorylation patterns across eukaryotes by discriminative N-gram analysis
Published in
BMC Bioinformatics, July 2015
DOI 10.1186/s12859-015-0657-2
Pubmed ID
Authors

Itziar Frades, Svante Resjö, Erik Andreasson

Abstract

How protein phosphorylation relates to kingdom/phylum divergence is largely unknown and the amino acid residues surrounding the phosphorylation site have profound importance on protein kinase-substrate interactions. Standard motif analysis is not adequate for large scale comparative analysis because each phophopeptide is assigned to a unique motif and perform poorly with the unbalanced nature of the input datasets. First the discriminative n-grams of five species from five different kingdom/phyla were identified. A signature with 5540 discriminative n-grams that could be found in other species from the same kingdoms/phyla was created. Using a test data set, the ability of the signature to classify species in their corresponding kingdom/phylum was confirmed using classification methods. Lastly, ortholog proteins among proteins with n-grams were identified in order to determine to what degree was the identity of the detected n-grams a property of phosphosites rather than a consequence of species-specific or kingdom/phylum-specific protein inventory. The motifs were grouped in clusters of equal physico-chemical nature and their distribution was similar between species in the same kingdom/phylum while clear differences were found among species of different kingdom/phylum. For example, the animal-specific top discriminative n-grams contained many basic amino acids and the plant-specific motifs were mainly acidic. Secondary structure prediction methods show that the discriminative n-grams in the majority of the cases lack from a regular secondary structure as on average they had 88 % of random coil compared to 66 % found in the phosphoproteins they were derived from. The discriminative n-grams were able to classify organisms in their corresponding kingdom/phylum, they show different patterns among species of different kingdom/phylum and these regions can contribute to evolutionary divergence as they are in disordered regions that can evolve rapidly. The differences found possibly reflect group-specific differences in the kinomes of the different groups of species.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 27%
Researcher 3 20%
Lecturer 2 13%
Student > Bachelor 1 7%
Other 1 7%
Other 2 13%
Unknown 2 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 33%
Agricultural and Biological Sciences 4 27%
Computer Science 3 20%
Pharmacology, Toxicology and Pharmaceutical Science 1 7%
Unknown 2 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 30 January 2016.
All research outputs
#15,340,815
of 22,818,766 outputs
Outputs from BMC Bioinformatics
#5,374
of 7,284 outputs
Outputs of similar age
#153,726
of 263,145 outputs
Outputs of similar age from BMC Bioinformatics
#75
of 108 outputs
Altmetric has tracked 22,818,766 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,284 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 263,145 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.