Creating an Open-Source Software Package for Quantitative PCR Analysis

Keywords: R, Software Documentation, Open Software, qPCR, molecular biology


Software is intended to be a tool used to complete tasks efficiently. However, the intent is not always matched by the execution as users may become frustrated with design idiosyncrasies or suboptimal implementations. In order to support reproducibility and the user experience, research software needs to be rewarded on its usability and documentation as well as  its functionality. Quality documentation needs to cover design decisions, functionality, and how to contribute to the software. Here we describe our work to tackle the reproducibility crisis in qPCR analysis by creating the open-source software package tidyqpcr. We also introduced the extensive infrastructure available for creating software documentation in the R programming language. 

Quantitative polymerase chain reaction (qPCR) is a fundamental technique in molecular biology to detect and quantify DNA and RNA. However, the ubiquitous use of qPCR across research disciplines has led to inconsistencies in implementation and reporting, leading to a reproducibility crisis and the publication of the Minimum Information in a Quantitative PCR experiment (MIQE) guidelines. In addition, each stage of a qPCR experiment can be customised to extract a wide variety of information from numerous biological processes. Developing versatile and reliable software built with best-practices and thorough documentation would promote reproducible qPCR analysis across diverse disciplines. tidyqpcr is an open source software package for user-friendly qPCR analysis using the tidyverse suite of R packages. tidyqpcr offers a consistent user interface and structure for qPCR analysis, within the tidyverse paradigm of spreadsheet-like rectangular data frames and generic functions that build up complex analyses in a series of simple steps. tidyqpcr focuses on experimental design in microwell plates, and relative quantification using changes in quantification cycle (∆Cq). tidyqpcr has been improved in response to software review from the rOpenSci non-profit initiative, which co-ordinates with the Journal of Open Source Software. Overall, tidyqpcr empowers scientists to conduct reproducible, flexible, and best-practice compliant quantitative PCR analysis.