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Differentiating Protein-Coding and Noncoding RNA: Challenges and Ambiguities

Overview of attention for article published in PLoS Computational Biology, November 2008
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users
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5 Wikipedia pages

Citations

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463 Dimensions

Readers on

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641 Mendeley
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15 CiteULike
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Title
Differentiating Protein-Coding and Noncoding RNA: Challenges and Ambiguities
Published in
PLoS Computational Biology, November 2008
DOI 10.1371/journal.pcbi.1000176
Pubmed ID
Authors

Marcel E. Dinger, Ken C. Pang, Tim R. Mercer, John S. Mattick

Abstract

The assumption that RNA can be readily classified into either protein-coding or non-protein-coding categories has pervaded biology for close to 50 years. Until recently, discrimination between these two categories was relatively straightforward: most transcripts were clearly identifiable as protein-coding messenger RNAs (mRNAs), and readily distinguished from the small number of well-characterized non-protein-coding RNAs (ncRNAs), such as transfer, ribosomal, and spliceosomal RNAs. Recent genome-wide studies have revealed the existence of thousands of noncoding transcripts, whose function and significance are unclear. The discovery of this hidden transcriptome and the implicit challenge it presents to our understanding of the expression and regulation of genetic information has made the need to distinguish between mRNAs and ncRNAs both more pressing and more complicated. In this Review, we consider the diverse strategies employed to discriminate between protein-coding and noncoding transcripts and the fundamental difficulties that are inherent in what may superficially appear to be a simple problem. Misannotations can also run in both directions: some ncRNAs may actually encode peptides, and some of those currently thought to do so may not. Moreover, recent studies have shown that some RNAs can function both as mRNAs and intrinsically as functional ncRNAs, which may be a relatively widespread phenomenon. We conclude that it is difficult to annotate an RNA unequivocally as protein-coding or noncoding, with overlapping protein-coding and noncoding transcripts further confounding this distinction. In addition, the finding that some transcripts can function both intrinsically at the RNA level and to encode proteins suggests a false dichotomy between mRNAs and ncRNAs. Therefore, the functionality of any transcript at the RNA level should not be discounted.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 641 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 2%
United Kingdom 7 1%
India 5 <1%
Australia 3 <1%
Netherlands 2 <1%
Mexico 2 <1%
Italy 1 <1%
Ireland 1 <1%
Norway 1 <1%
Other 14 2%
Unknown 594 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 169 26%
Researcher 127 20%
Student > Master 93 15%
Student > Bachelor 57 9%
Student > Doctoral Student 33 5%
Other 99 15%
Unknown 63 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 292 46%
Biochemistry, Genetics and Molecular Biology 178 28%
Medicine and Dentistry 28 4%
Computer Science 21 3%
Neuroscience 13 2%
Other 34 5%
Unknown 75 12%
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 25 October 2022.
All research outputs
#7,047,316
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#4,776
of 8,960 outputs
Outputs of similar age
#42,884
of 179,375 outputs
Outputs of similar age from PLoS Computational Biology
#21
of 40 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 46th percentile – i.e., 46% 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 179,375 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 75% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.