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Issues in current microRNA target identification methods

Overview of attention for article published in RNA Biology, May 2017
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Article details
Title
Issues in current microRNA target identification methods
Published in
RNA Biology, May 2017
DOI 10.1080/15476286.2017.1320469
Pubmed ID
Authors
Abstract

microRNAs are currently believed to control a large diversity of physiological processes, through the collective repression of thousands of target genes. Both experimental and computational analyses indeed suggest that each microRNA regulates tens or hundreds of genes. But some observations suggest that the phenotypic consequences of many published miRNA/mRNA interactions are dubious. For example, the reported amplitude of miRNA-guided repression is very small, while biological processes tend to be robust to small changes in gene expression. We recently showed, on one particular miRNA, that for most predicted targets, miRNA-guided repression is even smaller than inter-individual variability among wild-type specimens. We also put forward several sources of computational false positives. These issues are generally neglected by the scientific community, probably resulting in the frequent publication of irreproducible or misinterpreted results regarding microRNA function. We propose novel types of analyses, easily accessible to the community, that could help improve microRNA target identification.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 19 26%
Student > Master 14 19%
Student > Bachelor 9 12%
Researcher 9 12%
Professor > Associate Professor 2 3%
Other 4 5%
Unknown 16 22%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 36%
Agricultural and Biological Sciences 21 29%
Computer Science 2 3%
Neuroscience 2 3%
Chemistry 2 3%
Other 2 3%
Unknown 18 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 10 August 2017.
All research outputs
#18,542,806
of 22,965,074 outputs
Outputs from RNA Biology
#1,108
of 1,502 outputs
Outputs of similar age
#241,021
of 316,021 outputs
Outputs of similar age from RNA Biology
#16
of 27 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,502 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
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We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.