Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study.
Clicks: 342
ID: 94226
2014
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
342 views
50 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #9 of 61 articles by views in Nature Methods
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
MicroRNAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. Several measurement platforms have been developed to determine relative miRNA abundance in biological samples using different technologies such as small RNA sequencing, reverse transcription-quantitative PCR (RT-qPCR) and (microarray) hybridization. In this study, we systematically compared 12 commercially available platforms for analysis of microRNA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples and synthetic spikes from microRNA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specificity and concordance of differential expression. The results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative microRNA gene expression platform for particular study goals.
| Reference Key |
mestdagh2014evaluationnature
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Mestdagh, Pieter;Hartmann, Nicole;Baeriswyl, Lukas;Andreasen, Ditte;Bernard, Nathalie;Chen, Caifu;Cheo, David;D'Andrade, Petula;DeMayo, Mike;Dennis, Lucas;Derveaux, Stefaan;Feng, Yun;Fulmer-Smentek, Stephanie;Gerstmayer, Bernhard;Gouffon, Julia;Grimley, Chris;Lader, Eric;Lee, Kathy Y;Luo, Shujun;Mouritzen, Peter;Narayanan, Aishwarya;Patel, Sunali;Peiffer, Sabine;Rüberg, Silvia;Schroth, Gary;Schuster, Dave;Shaffer, Jonathan M;Shelton, Elliot J;Silveria, Scott;Ulmanella, Umberto;Veeramachaneni, Vamsi;Staedtler, Frank;Peters, Thomas;Guettouche, Toumy;Wong, Linda;Vandesompele, Jo; |
| Journal | Nature Methods |
| Year | 2014 |
| DOI |
10.1038/nmeth.3014
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.