RT Journal Article
SR Electronic
T1 Auto-qPCR: A Python-based web app for automated and reproducible analysis of qPCR data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.01.14.426748
DO 10.1101/2021.01.14.426748
A1 Gilles Maussion
A1 Rhalena A. Thomas
A1 Iveta Demirova
A1 Gracia Gu
A1 Eddie Cai
A1 Carol X.-Q Chen
A1 Narges Abdian
A1 Theodore J.P. Strauss
A1 Sabah Kelaï
A1 Angela Nauleau-Javaudin
A1 Lenore K. Beitel
A1 Nicolas Ramoz
A1 Philip Gorwood
A1 Thomas M. Durcan
YR 2021
UL http://biorxiv.org/content/early/2021/06/07/2021.01.14.426748.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.