With these technological advancements, qPCR is now used to quantify messenger RNA (mRNA) 11, long non-coding RNA 12, microRNAs 13, 14, DNA–protein interactions 15 and epigenetic modifications 16, 17. With the development of fluorogenic probes and dyes capable of binding newly synthesized DNA, PCR became more quantitative, leading to innovative tools for quantifying relative transcript levels for one or more genes, now referred to as quantitative PCR (qPCR).
Initially the technique was primarily used to visualize DNA fragments for cloning 2, 3 or genotyping 4, 5, 6, but can now be used to investigate genetic polymorphisms and mutations 7, 8, copy number variants (CNVs) 9, single nucleotide variants (SNVs), point mutations, and genetic deletion/duplication events 10. Polymerase chain reaction (PCR) identifies a nucleic acid fragment of interest by increasing its proportion relative to others 1. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs. Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample. Our web-based app ( ) is easy to use and does not require programming knowledge or software installation. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. Quantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols.
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