Abstract
Quantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. 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. 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. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app (https://auto-q-pcr.com/) is easy to use and does not require programming knowledge or software installation. 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 open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs.
Highlights
Abbreviations cycle threshold (CT) Cycle threshold quantitative Polymerase chain reaction (PCR) (qPCR) Quantitative polymerase chain reaction induced pluripotent stem cell (iPSC) Induced pluripotent stem cells copy number variants (CNVs) Copy number variants SNVs Single nucleotide variants DA Dopaminergic neural progenitor cells (NPCs) Neural precursor cells dopaminergic neurons (DANs) DA neurons
Nucleic acids are extracted from biological samples (RNA which is converted to cDNA for quantifying gene expression levels; or genomic DNA)
This paper presents Auto-quantitative PCR (qPCR), a new web app for qPCR analysis and provides examples of the functionalities of the software applied to qPCR experimental datasets generated from DNA, cDNA amplification, and RNA transcripts
Summary
Abbreviations CT Cycle threshold qPCR Quantitative polymerase chain reaction iPSC Induced pluripotent stem cells CNVs Copy number variants SNVs Single nucleotide variants DA Dopaminergic NPC Neural precursor cells DANs DA neurons. 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, referred to as quantitative PCR (qPCR). The user must intervene to include or exclude replicates, which, without guidelines or standardized procedures, can potentially introduce “user-dependent” variation and errors To both simplify and accelerate this data analysis step for qPCR datasets, we have created a Python-based, open source, user-friendly web application “Auto-qPCR” to process exported qPCR data and to provide summary tables, visual representations of the data, and statistical analysis. The program can work with the two commonly used molecular biology approaches: (i) absolute quantification, where all RNA estimations rely on orthogonal projection of the samples of interest onto a calibration curve[19], and (ii) relative quantification that relies on difference of cycle threshold (CT) values between the gene of interest and endogenous c ontrols[20]
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