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Extending gene ontology in the context of extracellular RNA and vesicle communication

Overview of attention for article published in Journal of Biomedical Semantics, April 2016
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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6 X users
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1 Facebook page

Citations

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27 Dimensions

Readers on

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98 Mendeley
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2 CiteULike
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Title
Extending gene ontology in the context of extracellular RNA and vesicle communication
Published in
Journal of Biomedical Semantics, April 2016
DOI 10.1186/s13326-016-0061-5
Pubmed ID
Authors

Kei-Hoi Cheung, Shivakumar Keerthikumar, Paola Roncaglia, Sai Lakshmi Subramanian, Matthew E. Roth, Monisha Samuel, Sushma Anand, Lahiru Gangoda, Stephen Gould, Roger Alexander, David Galas, Mark B. Gerstein, Andrew F. Hill, Robert R. Kitchen, Jan Lötvall, Tushar Patel, Dena C. Procaccini, Peter Quesenberry, Joel Rozowsky, Robert L. Raffai, Aleksandra Shypitsyna, Andrew I. Su, Clotilde Théry, Kasey Vickers, Marca H.M. Wauben, Suresh Mathivanan, Aleksandar Milosavljevic, Louise C. Laurent

Abstract

To address the lack of standard terminology to describe extracellular RNA (exRNA) data/metadata, we have launched an inter-community effort to extend the Gene Ontology (GO) with subcellular structure concepts relevant to the exRNA domain. By extending GO in this manner, the exRNA data/metadata will be more easily annotated and queried because it will be based on a shared set of terms and relationships relevant to extracellular research. By following a consensus-building process, we have worked with several academic societies/consortia, including ERCC, ISEV, and ASEMV, to identify and approve a set of exRNA and extracellular vesicle-related terms and relationships that have been incorporated into GO. In addition, we have initiated an ongoing process of extractions of gene product annotations associated with these terms from Vesiclepedia and ExoCarta, conversion of the extracted annotations to Gene Association File (GAF) format for batch submission to GO, and curation of the submitted annotations by the GO Consortium. As a use case, we have incorporated some of the GO terms into annotations of samples from the exRNA Atlas and implemented a faceted search interface based on such annotations. We have added 7 new terms and modified 9 existing terms (along with their synonyms and relationships) to GO. Additionally, 18,695 unique coding gene products (mRNAs and proteins) and 963 unique non-coding gene products (ncRNAs) which are associated with the terms: "extracellular vesicle", "extracellular exosome", "apoptotic body", and "microvesicle" were extracted from ExoCarta and Vesiclepedia. These annotations are currently being processed for submission to GO. As an inter-community effort, we have made a substantial update to GO in the exRNA context. We have also demonstrated the utility of some of the new GO terms for sample annotation and metadata search.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 2 2%
United States 1 1%
Belgium 1 1%
Unknown 94 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 22%
Researcher 15 15%
Student > Master 14 14%
Student > Bachelor 11 11%
Professor > Associate Professor 6 6%
Other 14 14%
Unknown 16 16%
Readers by discipline Count As %
Medicine and Dentistry 20 20%
Agricultural and Biological Sciences 19 19%
Biochemistry, Genetics and Molecular Biology 17 17%
Computer Science 4 4%
Engineering 4 4%
Other 14 14%
Unknown 20 20%
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 04 May 2016.
All research outputs
#6,461,428
of 23,344,526 outputs
Outputs from Journal of Biomedical Semantics
#117
of 367 outputs
Outputs of similar age
#90,649
of 302,113 outputs
Outputs of similar age from Journal of Biomedical Semantics
#6
of 15 outputs
Altmetric has tracked 23,344,526 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 367 research outputs from this source. They receive a mean Attention Score of 4.6. 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 302,113 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 69% of its contemporaries.
We're also able to compare this research output to 15 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 66% of its contemporaries.