Auto-qPCR; a python-based web app for automated and reproducible analysis of qPCR data

• Published in: Scientific reports Vol. 11; no. 1; pp. 21293 - 14
• Main Authors: Maussion, Gilles, Thomas, Rhalena A., Demirova, Iveta, Gu, Gracia, Cai, Eddie, Chen, Carol X.-Q., Abdian, Narges, Strauss, Theodore J. P., Kelaï, Sabah, Nauleau-Javaudin, Angela, Beitel, Lenore K., Ramoz, Nicolas, Gorwood, Philip, Durcan, Thomas M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.10.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1314; 631/114/794; 631/1647/1513/2216; 631/1647/2017/2003; 631/1647/48; 631/337/2019; 631/532/1360; 631/532/2064; Algorithms; Automation; Biochemistry, Molecular Biology; Computational Biology - methods; Computer programs; Data Analysis; Data processing; Deoxyribonucleic acid; DNA; Gene deletion; Gene expression; Humanities and Social Sciences; Humans; Life Sciences; Molecular biology; multidisciplinary; Polymerase chain reaction; Quantitative Methods; Real-Time Polymerase Chain Reaction - methods; Risk reduction; Science; Science (multidisciplinary); Software; Statistical analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-99727-6

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.