MiRcomp-Shiny: Interactive assessment of qPCR-based microRNA quantification and quality control algorithms

Lauren Kemperman,Matthew N Mccall

doi:10.12688/f1000research.13098.1

Lauren Kemperman, Matthew N Mccall

Open Access

https://doi.org/10.12688/f1000research.13098.1

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Abstract

The miRcomp-Shiny web application allows interactive performance assessments and comparisons of qPCR-based microRNA expression and quality estimation methods using a benchmark data set. This work is motivated by two distinct use cases: (1) selection of methodology and quality thresholds for use analyzing one's own data, and (2) comparison of novel expression estimation algorithms with currently-available methodology. The miRcomp-Shiny application is implemented in the R/Shiny language and can be installed on any operating system on which R can be installed. It is made freely available as part of the miRcomp package (version 1.3.3 and later) available through the Bioconductor project at: http://bioconductor.org/packages/miRcomp. The web application is hosted at https://laurenkemperman.shinyapps.io/mircomp/. A detailed description of how to use the web application is available at: http://lkemperm.github.io/miRcomp_shiny_app

Highlights

Quantitative real-time PCR is one of the most widely used methods to measure the expression of a target transcript
Current methods to estimate expression from raw Quantitative real-time PCR (qPCR) amplification data have been developed in a variety of programming languages (e.g. R, Python, SAS) and may be restricted to a particular operating system (e.g. Windows, Mac OS, Unix/Linux)[6]
The development of a repository of qPCR-based miRNA expression estimation algorithms will be a valuable resource for researchers seeking to develop new methodology or comparison existing algorithms across a wide variety of assessment criteria

Summary

Introduction

Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure the expression of a target transcript. Current methods to estimate expression from raw qPCR amplification data have been developed in a variety of programming languages (e.g. R, Python, SAS) and may be restricted to a particular operating system (e.g. Windows, Mac OS, Unix/Linux)[6]. These algorithms often return different data structures, complicating comparisons between methods. The miRcomp-Shiny web application provides a unified assessment environment that is platform independent and takes simple expression and quality matrices as input This approach removes barriers to usage and facilitates the comparison of methods

Methods

Conclusion