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Cross Platform Standardisation of an Experimental Pipeline for Use in the Identification of Dysregulated Human Circulating MiRNAs

Overview of attention for article published in PLoS ONE, September 2015
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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4 tweeters

Citations

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5 Dimensions

Readers on

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26 Mendeley
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Title
Cross Platform Standardisation of an Experimental Pipeline for Use in the Identification of Dysregulated Human Circulating MiRNAs
Published in
PLoS ONE, September 2015
DOI 10.1371/journal.pone.0137389
Pubmed ID
Authors

Helena Kelly, Tim Downing, Nina L. Tuite, Terry J. Smith, Michael J. Kerin, Róisín M. Dwyer, Eoin Clancy, Thomas Barry, Kate Reddington

Abstract

Micro RNAs (miRNAs) are a class of highly conserved small non-coding RNAs that play an important part in the post-transcriptional regulation of gene expression. A substantial number of miRNAs have been proposed as biomarkers for diseases. While reverse transcriptase Real-time PCR (RT-qPCR) is considered the gold standard for the evaluation and validation of miRNA biomarkers, small RNA sequencing is now routinely being adopted for the identification of dysregulated miRNAs. However, in many cases where putative miRNA biomarkers are identified using small RNA sequencing, they are not substantiated when RT-qPCR is used for validation. To date, there is a lack of consensus regarding optimal methodologies for miRNA detection, quantification and standardisation when different platform technologies are used. In this study we present an experimental pipeline that takes into consideration sample collection, processing, enrichment, and the subsequent comparative analysis of circulating small ribonucleic acids using small RNA sequencing and RT-qPCR. Initially, a panel of miRNAs dysregulated in circulating blood from breast cancer patients compared to healthy women were identified using small RNA sequencing. MiR-320a was identified as the most dysregulated miRNA between the two female cohorts. Total RNA and enriched small RNA populations (<30 bp) isolated from peripheral blood from the same female cohort samples were then tested for using a miR-320a RT-qPCR assay. When total RNA was analysed with this miR-320a RT-qPCR assay, a 2.3-fold decrease in expression levels was observed between blood samples from healthy controls and breast cancer patients. However, upon enrichment for the small RNA population and subsequent analysis of miR-320a using RT-qPCR, its dysregulation in breast cancer patients was more pronounced with an 8.89-fold decrease in miR-320a expression. We propose that the experimental pipeline outlined could serve as a robust approach for the identification and validation of small RNA biomarkers for disease.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 4%
Unknown 25 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 27%
Student > Ph. D. Student 6 23%
Other 3 12%
Student > Bachelor 3 12%
Student > Master 1 4%
Other 2 8%
Unknown 4 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 35%
Biochemistry, Genetics and Molecular Biology 5 19%
Medicine and Dentistry 5 19%
Computer Science 1 4%
Immunology and Microbiology 1 4%
Other 1 4%
Unknown 4 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 02 February 2016.
All research outputs
#6,939,813
of 13,091,737 outputs
Outputs from PLoS ONE
#61,852
of 140,865 outputs
Outputs of similar age
#89,786
of 240,110 outputs
Outputs of similar age from PLoS ONE
#2,298
of 5,696 outputs
Altmetric has tracked 13,091,737 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 140,865 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.0. This one has gotten more attention than average, scoring higher than 54% 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 240,110 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 5,696 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.