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Long non-coding RNAs display higher natural expression variation than protein-coding genes in healthy humans

Overview of attention for article published in Genome Biology (Online Edition), January 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

news
2 news outlets
blogs
1 blog
twitter
34 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
99 Dimensions

Readers on

mendeley
238 Mendeley
citeulike
1 CiteULike
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Title
Long non-coding RNAs display higher natural expression variation than protein-coding genes in healthy humans
Published in
Genome Biology (Online Edition), January 2016
DOI 10.1186/s13059-016-0873-8
Pubmed ID
Authors

Aleksandra E. Kornienko, Christoph P. Dotter, Philipp M. Guenzl, Heinz Gisslinger, Bettina Gisslinger, Ciara Cleary, Robert Kralovics, Florian M. Pauler, Denise P. Barlow

Abstract

Long non-coding RNAs (lncRNAs) are increasingly implicated as gene regulators and may ultimately be more numerous than protein-coding genes in the human genome. Despite large numbers of reported lncRNAs, reference annotations are likely incomplete due to their lower and tighter tissue-specific expression compared to mRNAs. An unexplored factor potentially confounding lncRNA identification is inter-individual expression variability. Here, we characterize lncRNA natural expression variability in human primary granulocytes. We annotate granulocyte lncRNAs and mRNAs in RNA-seq data from 10 healthy individuals, identifying multiple lncRNAs absent from reference annotations, and use this to investigate three known features (higher tissue-specificity, lower expression, and reduced splicing efficiency) of lncRNAs relative to mRNAs. Expression variability was examined in seven individuals sampled three times at 1- or more than 1-month intervals. We show that lncRNAs display significantly more inter-individual expression variability compared to mRNAs. We confirm this finding in two independent human datasets by analyzing multiple tissues from the GTEx project and lymphoblastoid cell lines from the GEUVADIS project. Using the latter dataset we also show that including more human donors into the transcriptome annotation pipeline allows identification of an increasing number of lncRNAs, but minimally affects mRNA gene number. A comprehensive annotation of lncRNAs is known to require an approach that is sensitive to low and tight tissue-specific expression. Here we show that increased inter-individual expression variability is an additional general lncRNA feature to consider when creating a comprehensive annotation of human lncRNAs or proposing their use as prognostic or disease markers.

Twitter Demographics

The data shown below were collected from the profiles of 34 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 238 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
United Kingdom 2 <1%
Mexico 1 <1%
Finland 1 <1%
Chile 1 <1%
Sweden 1 <1%
Belgium 1 <1%
Russia 1 <1%
Spain 1 <1%
Other 2 <1%
Unknown 224 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 51 21%
Researcher 51 21%
Student > Postgraduate 37 16%
Student > Master 20 8%
Student > Bachelor 19 8%
Other 36 15%
Unknown 24 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 91 38%
Agricultural and Biological Sciences 77 32%
Medicine and Dentistry 12 5%
Computer Science 8 3%
Immunology and Microbiology 4 2%
Other 14 6%
Unknown 32 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 42. 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 October 2017.
All research outputs
#613,227
of 17,630,293 outputs
Outputs from Genome Biology (Online Edition)
#532
of 3,631 outputs
Outputs of similar age
#14,822
of 349,732 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 17,630,293 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,631 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one has done well, scoring higher than 85% 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 349,732 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% 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