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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, January 2016
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

news
2 news outlets
blogs
1 blog
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29 X users
facebook
1 Facebook page

Citations

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135 Dimensions

Readers on

mendeley
254 Mendeley
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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, 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 29 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 254 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
United Kingdom 2 <1%
Netherlands 1 <1%
Chile 1 <1%
Finland 1 <1%
Mexico 1 <1%
Sweden 1 <1%
Russia 1 <1%
Belgium 1 <1%
Other 2 <1%
Unknown 241 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 53 21%
Researcher 53 21%
Student > Postgraduate 38 15%
Student > Master 20 8%
Student > Bachelor 19 7%
Other 42 17%
Unknown 29 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 96 38%
Agricultural and Biological Sciences 76 30%
Medicine and Dentistry 15 6%
Computer Science 11 4%
Immunology and Microbiology 4 2%
Other 16 6%
Unknown 36 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 39. 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
#1,048,264
of 25,374,917 outputs
Outputs from Genome Biology
#754
of 4,467 outputs
Outputs of similar age
#18,774
of 405,221 outputs
Outputs of similar age from Genome Biology
#15
of 65 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 83% 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 405,221 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 65 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.