Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-08

AUTHORS

Pieter Mestdagh, Nicole Hartmann, Lukas Baeriswyl, Ditte Andreasen, Nathalie Bernard, Caifu Chen, David Cheo, Petula D'Andrade, Mike DeMayo, Lucas Dennis, Stefaan Derveaux, Yun Feng, Stephanie Fulmer-Smentek, Bernhard Gerstmayer, Julia Gouffon, Chris Grimley, Eric Lader, Kathy Y Lee, Shujun Luo, Peter Mouritzen, Aishwarya Narayanan, Sunali Patel, Sabine Peiffer, Silvia Rüberg, Gary Schroth, Dave Schuster, Jonathan M Shaffer, Elliot J Shelton, Scott Silveria, Umberto Ulmanella, Vamsi Veeramachaneni, Frank Staedtler, Thomas Peters, Toumy Guettouche, Linda Wong, Jo Vandesompele

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. More... »

PAGES

809-815

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nmeth.3014

DOI

http://dx.doi.org/10.1038/nmeth.3014

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045449900

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24973947


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