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Detecting small plant peptides using SPADA (Small Peptide Alignment Discovery Application)

Overview of attention for article published in BMC Bioinformatics, November 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

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8 X users
googleplus
1 Google+ user

Citations

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86 Dimensions

Readers on

mendeley
134 Mendeley
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1 CiteULike
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Title
Detecting small plant peptides using SPADA (Small Peptide Alignment Discovery Application)
Published in
BMC Bioinformatics, November 2013
DOI 10.1186/1471-2105-14-335
Pubmed ID
Authors

Peng Zhou, Kevin AT Silverstein, Liangliang Gao, Jonathan D Walton, Sumitha Nallu, Joseph Guhlin, Nevin D Young

Abstract

Small peptides encoded as one- or two-exon genes in plants have recently been shown to affect multiple aspects of plant development, reproduction and defense responses. However, popular similarity search tools and gene prediction techniques generally fail to identify most members belonging to this class of genes. This is largely due to the high sequence divergence among family members and the limited availability of experimentally verified small peptides to use as training sets for homology search and ab initio prediction. Consequently, there is an urgent need for both experimental and computational studies in order to further advance the accurate prediction of small peptides.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Norway 1 <1%
France 1 <1%
Sri Lanka 1 <1%
Israel 1 <1%
Unknown 127 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 23%
Researcher 31 23%
Student > Bachelor 13 10%
Student > Master 10 7%
Student > Doctoral Student 8 6%
Other 22 16%
Unknown 19 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 63 47%
Biochemistry, Genetics and Molecular Biology 24 18%
Computer Science 8 6%
Medicine and Dentistry 3 2%
Engineering 3 2%
Other 7 5%
Unknown 26 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 13 June 2014.
All research outputs
#6,390,404
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#2,380
of 7,418 outputs
Outputs of similar age
#72,697
of 306,092 outputs
Outputs of similar age from BMC Bioinformatics
#32
of 104 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 67% 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 306,092 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 104 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 69% of its contemporaries.