Abstract
BackgroundSeveral 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.ResultsIn 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.ConclusionsBenchmark 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0987-8) contains supplementary material, which is available to authorized users.
Highlights
Several techniques have been tailored to the quantification of microRNA expression, including hybridization arrays, quantitative PCR, and high-throughput sequencing
The first method is an algorithm developed by Life Technologies and implemented in the ExpressionSuite software package
For clarity of presentation in this manuscript, we will focus on comparing two widely-used algorithms, the default algorithms from Life Technologies and the qpcR R package, in the following results
Summary
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. Originally designed to quantify mRNA expression, have been adapted to globally assay miRNA expression including hybridization arrays, quantitative PCR (qPCR), and sequencing [6]. Each of these approaches must overcome several challenges specific to miRNAs: short sequence length, low abundance of target molecules, and sequence homology between miRNAs. Comparative performance assessments are crucial to understanding the strengths and limitations of each approach to miRNA quantification. A single platform was not found to be uniformly superior, there
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