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Non-coding yet non-trivial: a review on the computational genomics of lincRNAs

Overview of attention for article published in BioData Mining, December 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
67 Mendeley
citeulike
2 CiteULike

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Summary Twitter Dimensions citations
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Title
Non-coding yet non-trivial: a review on the computational genomics of lincRNAs
Published in
BioData Mining, December 2015
DOI 10.1186/s13040-015-0075-z
Pubmed ID
26697116
Authors

Travers Ching, Jayson Masaki, Jason Weirather, Lana X. Garmire

Abstract

Long intergenic non-coding RNAs (lincRNAs) represent one of the most mysterious RNA species encoded by the human genome. Thanks to next generation sequencing (NGS) technology and its applications, we have recently witnessed a surge in non-coding RNA research, including lincRNA research. Here, we summarize the recent advancement in genomics studies of lincRNAs. We review the emerging characteristics of lincRNAs, the experimental and computational approaches to identify lincRNAs, their known mechanisms of regulation, the computational methods and resources for lincRNA functional predictions, and discuss the challenges to understanding lincRNA comprehensively.

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Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
India 1 1%
China 1 1%
Norway 1 1%
Unknown 62 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 22%
Researcher 15 22%
Student > Bachelor 8 12%
Student > Master 7 10%
Student > Doctoral Student 4 6%
Other 9 13%
Unknown 9 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 39%
Biochemistry, Genetics and Molecular Biology 12 18%
Computer Science 7 10%
Medicine and Dentistry 5 7%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Other 5 7%
Unknown 10 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 28 December 2015.
All research outputs
#9,485,605
of 17,800,904 outputs
Outputs from BioData Mining
#155
of 277 outputs
Outputs of similar age
#144,292
of 376,724 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 17,800,904 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 277 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 42nd percentile – i.e., 42% 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 376,724 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 61% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them

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