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FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

Overview of attention for article published in Journal of Biomedical Semantics, June 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#31 of 364)
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

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Title
FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation
Published in
Journal of Biomedical Semantics, June 2016
DOI 10.1186/s13326-016-0067-z
Pubmed ID
Authors

Jerven T. Bolleman, Christopher J. Mungall, Francesco Strozzi, Joachim Baran, Michel Dumontier, Raoul J. P. Bonnal, Robert Buels, Robert Hoehndorf, Takatomo Fujisawa, Toshiaki Katayama, Peter J. A. Cock

Abstract

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. We have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned "omics" areas. Using the same data format to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe - and potentially merge - sequence annotations from multiple sources. Data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.

X Demographics

X Demographics

The data shown below were collected from the profiles of 21 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 9%
Japan 2 3%
Canada 2 3%
Italy 1 2%
Brazil 1 2%
Sweden 1 2%
Spain 1 2%
Unknown 45 78%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 34%
Professor > Associate Professor 9 16%
Student > Ph. D. Student 8 14%
Student > Master 5 9%
Other 4 7%
Other 6 10%
Unknown 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 45%
Biochemistry, Genetics and Molecular Biology 10 17%
Computer Science 8 14%
Engineering 2 3%
Chemistry 1 2%
Other 1 2%
Unknown 10 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 January 2020.
All research outputs
#2,176,912
of 22,877,793 outputs
Outputs from Journal of Biomedical Semantics
#31
of 364 outputs
Outputs of similar age
#41,794
of 352,763 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 22 outputs
Altmetric has tracked 22,877,793 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 91% 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 352,763 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 88% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.