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A benchmark for microRNA quantification algorithms using the OpenArray platform

Overview of attention for article published in BMC Bioinformatics, March 2016
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  • Above-average Attention Score compared to outputs of the same age (63rd 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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6 Dimensions

Readers on

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36 Mendeley
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Title
A benchmark for microRNA quantification algorithms using the OpenArray platform
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0987-8
Pubmed ID
Authors

Matthew N. McCall, Alexander S. Baras, Alexander Crits-Christoph, Roxann Ingersoll, Melissa A. McAlexander, Kenneth W. Witwer, Marc K. Halushka

Abstract

Several techniques have been tailored to the quantification of microRNA expression, including hybridization arrays, quantitative PCR (qPCR), and high-throughput sequencing. Each of these has certain strengths and limitations depending both on the technology itself and the algorithm used to convert raw data into expression estimates. Reliable quantification of microRNA expression is challenging in part due to the relatively low abundance and short length of the miRNAs. While substantial research has been devoted to the development of methods to quantify mRNA expression, relatively little effort has been spent on microRNA expression. In this work, we focus on the Life Technologies TaqMan OpenArray(Ⓡ) system, a qPCR-based platform to measure microRNA expression. Several algorithms currently exist to estimate expression from the raw amplification data produced by qPCR-based technologies. To assess and compare the performance of these methods, we performed a set of dilution/mixture experiments to create a benchmark data set. We also developed a suite of statistical assessments that evaluate many different aspects of performance: accuracy, precision, titration response, number of complete features, limit of detection, and data quality. The benchmark data and software are freely available via two R/Bioconductor packages, miRcomp and miRcompData. Finally, we demonstrate use of our software by comparing two widely used algorithms and providing assessments for four other algorithms. Benchmark data sets and software are crucial tools for the assessment and comparison of competing algorithms. We believe that the miRcomp and miRcompData packages will facilitate the development of new methodology for microRNA expression estimation.

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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 3%
United States 1 3%
Denmark 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 31%
Student > Ph. D. Student 6 17%
Professor > Associate Professor 4 11%
Professor 3 8%
Student > Postgraduate 2 6%
Other 5 14%
Unknown 5 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 25%
Biochemistry, Genetics and Molecular Biology 7 19%
Medicine and Dentistry 4 11%
Computer Science 3 8%
Neuroscience 2 6%
Other 4 11%
Unknown 7 19%

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 29 March 2016.
All research outputs
#5,718,352
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#2,015
of 4,195 outputs
Outputs of similar age
#104,493
of 287,118 outputs
Outputs of similar age from BMC Bioinformatics
#63
of 130 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 50% 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 287,118 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 63% of its contemporaries.
We're also able to compare this research output to 130 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 50% of its contemporaries.