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Summarizing performance for genome scale measurement of miRNA: reference samples and metrics

Overview of attention for article published in BMC Genomics, March 2018
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  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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5 tweeters

Citations

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3 Dimensions

Readers on

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28 Mendeley
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Title
Summarizing performance for genome scale measurement of miRNA: reference samples and metrics
Published in
BMC Genomics, March 2018
DOI 10.1186/s12864-018-4496-1
Pubmed ID
Authors

P. Scott Pine, Steven P. Lund, Jerod R. Parsons, Lindsay K. Vang, Ashish A. Mahabal, Luca Cinquini, Sean C. Kelly, Heather Kincaid, Daniel J. Crichton, Avrum Spira, Gang Liu, Adam C. Gower, Harvey I. Pass, Chandra Goparaju, Steven M. Dubinett, Kostyantyn Krysan, Sanford A. Stass, Debra Kukuruga, Kendall Van Keuren-Jensen, Amanda Courtright-Lim, Karol L. Thompson, Barry A. Rosenzweig, Lynn Sorbara, Sudhir Srivastava, Marc L. Salit

Abstract

The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls. Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes. The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 29%
Student > Bachelor 4 14%
Researcher 3 11%
Student > Master 3 11%
Professor > Associate Professor 2 7%
Other 6 21%
Unknown 2 7%
Readers by discipline Count As %
Medicine and Dentistry 8 29%
Biochemistry, Genetics and Molecular Biology 3 11%
Engineering 3 11%
Agricultural and Biological Sciences 3 11%
Computer Science 2 7%
Other 6 21%
Unknown 3 11%

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 10 December 2018.
All research outputs
#7,407,382
of 14,009,143 outputs
Outputs from BMC Genomics
#3,513
of 8,192 outputs
Outputs of similar age
#120,871
of 275,958 outputs
Outputs of similar age from BMC Genomics
#7
of 12 outputs
Altmetric has tracked 14,009,143 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 8,192 research outputs from this source. They receive a mean Attention Score of 4.2. This one has gotten more attention than average, scoring higher than 55% 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 275,958 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 55% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.